{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Las-c8I5uBVi",
    "outputId": "0d66c7be-9ead-4f93-fc44-e76aa2380cf3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\asus\\music_classification\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "c:\\users\\asus\\music_classification\\lib\\site-packages\\numpy\\.libs\\libopenblas.QVLO2T66WEPI7JZ63PS3HMOHFEY472BC.gfortran-win_amd64.dll\n",
      "c:\\users\\asus\\music_classification\\lib\\site-packages\\numpy\\.libs\\libopenblas.WCDJNK7YVMPZQ2ME2ZZHJJRJ3JIKNDB7.gfortran-win_amd64.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    }
   ],
   "source": [
    " \n",
    "import numpy as np\n",
    "from numpy import array\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import string\n",
    "import os\n",
    "from PIL import Image\n",
    "import glob\n",
    "from pickle import dump, load\n",
    "from time import time\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Embedding, TimeDistributed, Dense, RepeatVector,\\\n",
    "                         Activation, Flatten, Reshape, concatenate, Dropout, BatchNormalization\n",
    "from keras.optimizers import Adam, RMSprop\n",
    "from keras.layers.wrappers import Bidirectional\n",
    "from keras.layers.merge import add\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.preprocessing import image\n",
    "from keras.models import Model\n",
    "from keras import Input, layers\n",
    "from keras import optimizers\n",
    "from keras.applications.inception_v3 import preprocess_input\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "physical_devices = tf.config.list_physical_devices('GPU')\n",
    "tf.config.experimental.set_memory_growth(physical_devices[0], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "id": "qoSO1_b-uBWE",
    "outputId": "d28b1179-7130-4626-c498-d7633003e4b9"
   },
   "outputs": [],
   "source": [
    "# load doc into memory\n",
    "def load_doc(filename):\n",
    "    # open the file as read only\n",
    "    file = open(filename, 'r')\n",
    "    # read all text\n",
    "    text = file.read()\n",
    "    # close the file\n",
    "    file.close()\n",
    "    return text\n",
    " \n",
    "filename = \"Flickr8k_text/Flickr8k.token.txt\"\n",
    "# load descriptions\n",
    "doc = load_doc(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "D7euwhC1uBWa",
    "outputId": "53a413fa-1a41-4d49-d08f-a7a51b29bdd6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded: 8092 \n"
     ]
    }
   ],
   "source": [
    "def load_descriptions(doc):\n",
    "    mapping = dict()\n",
    "    # process lines\n",
    "    for line in doc.split('\\n'):\n",
    "        # split line by white space\n",
    "        tokens = line.split()\n",
    "        if len(line) < 2:\n",
    "            continue\n",
    "        # take the first token as the image id, the rest as the description\n",
    "        image_id, image_desc = tokens[0], tokens[1:]\n",
    "        # extract filename from image id\n",
    "        image_id = image_id.split('.')[0]\n",
    "        # convert description tokens back to string\n",
    "        image_desc = ' '.join(image_desc)\n",
    "        # create the list if needed\n",
    "        if image_id not in mapping:\n",
    "            mapping[image_id] = list()\n",
    "        # store description\n",
    "        mapping[image_id].append(image_desc)\n",
    "    return mapping\n",
    " \n",
    "# parse descriptions\n",
    "descriptions = load_descriptions(doc)\n",
    "print('Loaded: %d ' % len(descriptions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "id": "my-rPVaGuBWv",
    "outputId": "243d5144-bb00-470b-9557-147ff2111387"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1000268201_693b08cb0e',\n",
       " '1001773457_577c3a7d70',\n",
       " '1002674143_1b742ab4b8',\n",
       " '1003163366_44323f5815',\n",
       " '1007129816_e794419615']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list(descriptions.keys())[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "id": "JUIbKdxNuBXE",
    "outputId": "98cbe971-aba0-4a5c-9ab7-31d4c56c240e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A child in a pink dress is climbing up a set of stairs in an entry way .',\n",
       " 'A girl going into a wooden building .',\n",
       " 'A little girl climbing into a wooden playhouse .',\n",
       " 'A little girl climbing the stairs to her playhouse .',\n",
       " 'A little girl in a pink dress going into a wooden cabin .']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptions['1000268201_693b08cb0e']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "id": "sskShrLDuBXb",
    "outputId": "77da831b-7873-4a2c-ae2b-5c663cabb10d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A black dog and a spotted dog are fighting',\n",
       " 'A black dog and a tri-colored dog playing with each other on the road .',\n",
       " 'A black dog and a white dog with brown spots are staring at each other in the street .',\n",
       " 'Two dogs of different breeds looking at each other on the road .',\n",
       " 'Two dogs on pavement moving toward each other .']"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptions['1001773457_577c3a7d70']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "S62c_TIeuBXy"
   },
   "outputs": [],
   "source": [
    "def clean_descriptions(descriptions):\n",
    "    # prepare translation table for removing punctuation\n",
    "    table = str.maketrans('', '', string.punctuation)\n",
    "    for key, desc_list in descriptions.items():\n",
    "        for i in range(len(desc_list)):\n",
    "            desc = desc_list[i]\n",
    "            # tokenize\n",
    "            desc = desc.split()\n",
    "            # convert to lower case\n",
    "            desc = [word.lower() for word in desc]\n",
    "            # remove punctuation from each token\n",
    "            desc = [w.translate(table) for w in desc]\n",
    "            # remove hanging 's' and 'a'\n",
    "            desc = [word for word in desc if len(word)>1]\n",
    "            # remove tokens with numbers in them\n",
    "            desc = [word for word in desc if word.isalpha()]\n",
    "            # store as string\n",
    "            desc_list[i] =  ' '.join(desc)\n",
    " \n",
    "# clean descriptions\n",
    "clean_descriptions(descriptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "id": "wMLEGCszuBYM",
    "outputId": "50aef490-93db-4d0b-b3d4-5f330ebeb964"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['child in pink dress is climbing up set of stairs in an entry way',\n",
       " 'girl going into wooden building',\n",
       " 'little girl climbing into wooden playhouse',\n",
       " 'little girl climbing the stairs to her playhouse',\n",
       " 'little girl in pink dress going into wooden cabin']"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptions['1000268201_693b08cb0e']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "id": "WPN-XnHFuBYh",
    "outputId": "d11a1f71-3ba8-40c8-b048-31eb757e6ba7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['black dog and spotted dog are fighting',\n",
       " 'black dog and tricolored dog playing with each other on the road',\n",
       " 'black dog and white dog with brown spots are staring at each other in the street',\n",
       " 'two dogs of different breeds looking at each other on the road',\n",
       " 'two dogs on pavement moving toward each other']"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptions['1001773457_577c3a7d70']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "id": "o886EKWLXKUz",
    "outputId": "05c5265b-7134-4f62-f4e9-4ff8502b3e85"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['child and woman are at waters edge in big city',\n",
       " 'large lake with lone duck swimming in it with several people around the edge of it',\n",
       " 'little boy at lake watching duck',\n",
       " 'young boy waves his hand at the duck in the water surrounded by green park',\n",
       " 'two people are at the edge of lake facing the water and the city skyline']"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptions['1022454332_6af2c1449a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "Q783lm48uBY1",
    "outputId": "38a00f4e-fa8f-4305-ce91-65bcceb0b23a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Vocabulary Size: 8763\n"
     ]
    }
   ],
   "source": [
    "# convert the loaded descriptions into a vocabulary of words\n",
    "def to_vocabulary(descriptions):\n",
    "    # build a list of all description strings\n",
    "    all_desc = set()\n",
    "    for key in descriptions.keys():\n",
    "        [all_desc.update(d.split()) for d in descriptions[key]]\n",
    "    return all_desc\n",
    " \n",
    "# summarize vocabulary\n",
    "vocabulary = to_vocabulary(descriptions)\n",
    "print('Original Vocabulary Size: %d' % len(vocabulary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "UW2mQ3AZuBZJ"
   },
   "outputs": [],
   "source": [
    "# save descriptions to file, one per line\n",
    "def save_descriptions(descriptions, filename):\n",
    "    lines = list()\n",
    "    for key, desc_list in descriptions.items():\n",
    "        for desc in desc_list:\n",
    "            lines.append(key + ' ' + desc)\n",
    "    data = '\\n'.join(lines)\n",
    "    file = open(filename, 'w')\n",
    "    file.write(data)\n",
    "    file.close()\n",
    " \n",
    "save_descriptions(descriptions, 'descriptions.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "xs_sX9oRuBZ6",
    "outputId": "9eb0c673-0d87-4181-ba80-09f70e49a321"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: 6000\n"
     ]
    }
   ],
   "source": [
    "# load a pre-defined list of photo identifiers\n",
    "def load_set(filename):\n",
    "    doc = load_doc(filename)\n",
    "    dataset = list()\n",
    "    # process line by line\n",
    "    for line in doc.split('\\n'):\n",
    "        # skip empty lines\n",
    "        if len(line) < 1:\n",
    "            continue\n",
    "        # get the image identifier\n",
    "        identifier = line.split('.')[0]\n",
    "        dataset.append(identifier)\n",
    "    return set(dataset)\n",
    " \n",
    "# load training dataset (6K)\n",
    "filename = 'Flickr8k_text/Flickr_8k.trainImages.txt'\n",
    "train = load_set(filename)\n",
    "print('Dataset: %d' % len(train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "cjSGXn1AuBaz"
   },
   "outputs": [],
   "source": [
    "# Below path contains all the images\n",
    "images = 'Flickr8k_Dataset/img/'\n",
    "# Create a list of all image names in the directory\n",
    "img = glob.glob(images + '*.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "VIQgS0ORuBbl"
   },
   "outputs": [],
   "source": [
    "# Below file conatains the names of images to be used in train data\n",
    "train_images_file = 'Flickr8k_text/Flickr_8k.trainImages.txt'\n",
    "# Read the train image names in a set\n",
    "train_images = set(open(train_images_file, 'r').read().strip().split('\\n'))\n",
    " \n",
    "# Create a list of all the training images with their full path names\n",
    "train_img = []\n",
    " \n",
    "for i in img: # img is list of full path names of all images\n",
    "    if i[len(images):] in train_images: # Check if the image belongs to training set\n",
    "        train_img.append(i) # Add it to the list of train images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "rk5pqFZZuBcc"
   },
   "outputs": [],
   "source": [
    "# Below file conatains the names of images to be used in test data\n",
    "test_images_file = 'Flickr8k_text/Flickr_8k.testImages.txt'\n",
    "# Read the validation image names in a set# Read the test image names in a set\n",
    "test_images = set(open(test_images_file, 'r').read().strip().split('\\n'))\n",
    " \n",
    "# Create a list of all the test images with their full path names\n",
    "test_img = []\n",
    " \n",
    "for i in img: # img is list of full path names of all images\n",
    "    if i[len(images):] in test_images: # Check if the image belongs to test set\n",
    "        test_img.append(i) # Add it to the list of test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "Aq2nKmZVuBdD",
    "outputId": "3fd8e33c-f4a6-452e-8817-7d7ecaa60726"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Descriptions: train=6000\n"
     ]
    }
   ],
   "source": [
    "# load clean descriptions into memory\n",
    "def load_clean_descriptions(filename, dataset):\n",
    "    # load document\n",
    "    doc = load_doc(filename)\n",
    "    descriptions = dict()\n",
    "    for line in doc.split('\\n'):\n",
    "        # split line by white space\n",
    "        tokens = line.split()\n",
    "        # split id from description\n",
    "        image_id, image_desc = tokens[0], tokens[1:]\n",
    "        # skip images not in the set\n",
    "        if image_id in dataset:\n",
    "            # create list\n",
    "            if image_id not in descriptions:\n",
    "                descriptions[image_id] = list()\n",
    "            # wrap description in tokens\n",
    "            desc = 'startseq ' + ' '.join(image_desc) + ' endseq'\n",
    "            # store\n",
    "            descriptions[image_id].append(desc)\n",
    "    return descriptions\n",
    " \n",
    "# descriptions\n",
    "train_descriptions = load_clean_descriptions('descriptions.txt', train)\n",
    "print('Descriptions: train=%d' % len(train_descriptions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "XM6_5PT_uBdY"
   },
   "outputs": [],
   "source": [
    "def preprocess(image_path):\n",
    "    # Convert all the images to size 299x299 as expected by the inception v3 model\n",
    "    img = image.load_img(image_path, target_size=(299, 299))\n",
    "    # Convert PIL image to numpy array of 3-dimensions\n",
    "    x = image.img_to_array(img)\n",
    "    # Add one more dimension\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    # preprocess the images using preprocess_input() from inception module\n",
    "    x = preprocess_input(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "2lBtida1uBdr",
    "outputId": "9c8cc9fe-8ab8-4061-be19-60e16ff83db3"
   },
   "outputs": [],
   "source": [
    "# Load the inception v3 model\n",
    "model = InceptionV3(weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a5a4f31792440778fc354971e51766c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.device('/gpu:0'):\n",
    "    config = tf.compat.v1.ConfigProto(gpu_options=\n",
    "                                      tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=1)\n",
    "                                      # device_count = {'GPU': 1}\n",
    "                                      )\n",
    "    config.gpu_options.allow_growth = True\n",
    "    session = tf.compat.v1.Session(config=config)\n",
    "    tf.compat.v1.keras.backend.set_session(session)\n",
    "    tqdm().pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "xK_S4l0NuBeH"
   },
   "outputs": [],
   "source": [
    "# Create a new model, by removing the last layer (output layer) from the inception v3\n",
    "model_new = Model(model.input, model.layers[-2].output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "azmiW2_quBel"
   },
   "outputs": [],
   "source": [
    "# Function to encode a given image into a vector of size (2048, )\n",
    "def encode(image):\n",
    "    image = preprocess(image) # preprocess the image\n",
    "    fea_vec = model_new.predict(image) # Get the encoding vector for the image\n",
    "    fea_vec = np.reshape(fea_vec, fea_vec.shape[1]) # reshape from (1, 2048) to (2048, )\n",
    "    return fea_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "k69Nza4kuBe-",
    "outputId": "f6a604e7-aac4-4bfa-bee5-d781007f2bf3"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6eb3e7ebcd68443b83f11c427d7bd043",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/6000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken in seconds = 624.8944821357727\n"
     ]
    }
   ],
   "source": [
    "# Call the funtion to encode all the train images\n",
    "# This will take a while on CPU - Execute this only once\n",
    "\n",
    "start = time()\n",
    "encoding_train = {}\n",
    "for img in tqdm(train_img):\n",
    "    encoding_train[img[len(images):]] = encode(img)\n",
    "print(\"Time taken in seconds =\", time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "iotf0t9FuBf0"
   },
   "outputs": [],
   "source": [
    "# Save the bottleneck train features to disk\n",
    "import pickle\n",
    "with open(\"featuresTrain_Inception.p\", \"wb\") as encoded_pickle:\n",
    "    pickle.dump(encoding_train, encoded_pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "id": "rfVQp0cwuBgE",
    "outputId": "7a45b617-4656-4662-b14a-654400eac65b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "083262ebbcc9400abf74f424546df9a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken in seconds = 108.94350910186768\n"
     ]
    }
   ],
   "source": [
    "# Call the funtion to encode all the test images - Execute this only once\n",
    "start = time()\n",
    "encoding_test = {}\n",
    "for img in tqdm(test_img):\n",
    "    encoding_test[img[len(images):]] = encode(img)\n",
    "print(\"Time taken in seconds =\", time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "BUGDevpFuBgU"
   },
   "outputs": [],
   "source": [
    "# Save the bottleneck test features to disk\n",
    "with open(\"featuresTest_Inception.p\", \"wb\") as encoded_pickle:\n",
    "    pickle.dump(encoding_test, encoded_pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "6VI9Oe1euBgo",
    "outputId": "ba5e1c55-84ba-457c-876c-2f5c00f320b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Photos: train=6000\n"
     ]
    }
   ],
   "source": [
    "train_features = load(open(\"featuresTrain_Inception.p\", \"rb\"))\n",
    "print('Photos: train=%d' % len(train_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "RIl3fPUCuBg7",
    "outputId": "3cc58e74-77b1-458b-fa12-5990b8143744"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of all the training captions\n",
    "all_train_captions = []\n",
    "for key, val in train_descriptions.items():\n",
    "    for cap in val:\n",
    "        all_train_captions.append(cap)\n",
    "len(all_train_captions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "QvxrI6l3uBhK",
    "outputId": "05b2c974-58a6-4459-8a43-ef7cdc499f2f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preprocessed words 7578 -> 1651\n"
     ]
    }
   ],
   "source": [
    "# Consider only words which occur at least 10 times in the corpus\n",
    "word_count_threshold = 10\n",
    "word_counts = {}\n",
    "nsents = 0\n",
    "for sent in all_train_captions:\n",
    "    nsents += 1\n",
    "    for w in sent.split(' '):\n",
    "        word_counts[w] = word_counts.get(w, 0) + 1\n",
    "\n",
    "vocab = [w for w in word_counts if word_counts[w] >= word_count_threshold]\n",
    "print('preprocessed words %d -> %d' % (len(word_counts), len(vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "LcmjDejquBhd"
   },
   "outputs": [],
   "source": [
    "ixtoword = {}\n",
    "wordtoix = {}\n",
    "\n",
    "ix = 1\n",
    "for w in vocab:\n",
    "    wordtoix[w] = ix\n",
    "    ixtoword[ix] = w\n",
    "    ix += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "tSKn3XSCuBhs",
    "outputId": "126dfaf4-b252-4be0-e353-08e62ad14cb6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1652"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_size = len(ixtoword) + 1 # one for appended 0's\n",
    "vocab_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "EvR0JdnVuBh7",
    "outputId": "d990e02f-4a38-4ec3-907d-f19e64a0569a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Description Length: 34\n"
     ]
    }
   ],
   "source": [
    "# convert a dictionary of clean descriptions to a list of descriptions\n",
    "def to_lines(descriptions):\n",
    "    all_desc = list()\n",
    "    for key in descriptions.keys():\n",
    "        [all_desc.append(d) for d in descriptions[key]]\n",
    "    return all_desc\n",
    "\n",
    "# calculate the length of the description with the most words\n",
    "def max_length(descriptions):\n",
    "    lines = to_lines(descriptions)\n",
    "    return max(len(d.split()) for d in lines)\n",
    "\n",
    "# determine the maximum sequence length\n",
    "max_length = max_length(train_descriptions)\n",
    "print('Description Length: %d' % max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "_Sd0H5zCuBiK"
   },
   "outputs": [],
   "source": [
    "# data generator, intended to be used in a call to model.fit_generator()\n",
    "def data_generator(descriptions, photos, wordtoix, max_length, num_photos_per_batch):\n",
    "    X1, X2, y = list(), list(), list()\n",
    "    n=0\n",
    "    # loop for ever over images\n",
    "    while 1:\n",
    "        for key, desc_list in descriptions.items():\n",
    "            # retrieve the photo feature\n",
    "            try:\n",
    "              photo = train_features[key+'.jpg']\n",
    "            except Exception as e:\n",
    "              continue\n",
    "            n+=1\n",
    "            for desc in desc_list:\n",
    "                # encode the sequence\n",
    "                seq = [wordtoix[word] for word in desc.split(' ') if word in wordtoix]\n",
    "                # split one sequence into multiple X, y pairs\n",
    "                for i in range(1, len(seq)):\n",
    "                    # split into input and output pair\n",
    "                    in_seq, out_seq = seq[:i], seq[i]\n",
    "                    # pad input sequence\n",
    "                    in_seq = pad_sequences([in_seq], maxlen=max_length)[0]\n",
    "                    # encode output sequence\n",
    "                    out_seq = to_categorical([out_seq], num_classes=vocab_size)[0]\n",
    "                    # store\n",
    "                    X1.append(photo)\n",
    "                    X2.append(in_seq)\n",
    "                    y.append(out_seq)\n",
    "            # yield the batch data\n",
    "            if n==num_photos_per_batch:\n",
    "                yield [array(X1), array(X2)], array(y)\n",
    "                X1, X2, y = list(), list(), list()\n",
    "                n=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "fA2FBhNtX-Ea",
    "outputId": "3febbfd4-990a-4547-ec76-0534d12dc859"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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'3156991513_3bf03333d8.jpg', '3537201804_ce07aff237.jpg', '1388346434_524d0b6dfa.jpg', '213216174_0632af65a2.jpg', '3357937209_cf4a9512ac.jpg', '520491467_54cbc0a866.jpg', '2699733386_c346c87ea6.jpg', '1280320287_b2a4b9b7bd.jpg', '224273695_0b517bd0eb.jpg', '2470493181_2efbbf17bd.jpg', '3176498130_52ab9460b2.jpg', '244443352_d7636e1253.jpg', '84713990_d3f3cef78b.jpg', '3040051410_6205682ba3.jpg'])"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "id": "SpM8xeCp0yyb",
    "outputId": "0a84992a-bd94-41af-d0c0-3c78babc5a34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  glove.6B.zip\n",
      "  inflating: glove.6B.50d.txt        \n",
      "  inflating: glove.6B.100d.txt       \n",
      "  inflating: glove.6B.200d.txt       \n",
      "  inflating: glove.6B.300d.txt       \n"
     ]
    }
   ],
   "source": [
    "# !wget \"http://nlp.stanford.edu/data/glove.6B.zip\"\n",
    "!unzip glove.6B.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "yKPLrK5GuBie",
    "outputId": "55b58cc7-bb37-41c4-ca60-3e3d797366de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "# Load Glove vectors\n",
    "glove_dir = ''\n",
    "embeddings_index = {} # empty dictionary\n",
    "f = open(os.path.join(glove_dir, 'glove.6B.200d.txt'), encoding=\"utf-8\")\n",
    "\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "print('Found %s word vectors.' % len(embeddings_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "6XZDSBsxuBit"
   },
   "outputs": [],
   "source": [
    "embedding_dim = 200\n",
    "\n",
    "# Get 200-dim dense vector for each of the 10000 words in out vocabulary\n",
    "embedding_matrix = np.zeros((vocab_size, embedding_dim))\n",
    "\n",
    "for word, i in wordtoix.items():\n",
    "    #if i < max_words:\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # Words not found in the embedding index will be all zeros\n",
    "        embedding_matrix[i] = embedding_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "LGmhYtiEuBi7",
    "outputId": "3d29da63-46b8-4449-8004-7ad8a64c9bd9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1652, 200)"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "bGIcfbqKuBjb"
   },
   "outputs": [],
   "source": [
    "inputs1 = Input(shape=(2048,))\n",
    "fe1 = Dropout(0.5)(inputs1)\n",
    "fe2 = Dense(256, activation='relu')(fe1)\n",
    "inputs2 = Input(shape=(max_length,))\n",
    "se1 = Embedding(vocab_size, embedding_dim, mask_zero=True)(inputs2)\n",
    "se2 = Dropout(0.5)(se1)\n",
    "se3 = LSTM(256)(se2)\n",
    "decoder1 = add([fe2, se3])\n",
    "decoder2 = Dense(256, activation='relu')(decoder1)\n",
    "outputs = Dense(vocab_size, activation='softmax')(decoder2)\n",
    "model = Model(inputs=[inputs1, inputs2], outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "id": "atuUTdX7uBjq",
    "outputId": "c76226cb-01d9-4529-d8c4-3559d51efa61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            [(None, 34)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            [(None, 2048)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, 34, 200)      330400      input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 2048)         0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 34, 200)      0           embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 256)          524544      dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm (LSTM)                     (None, 256)          467968      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 256)          0           dense[0][0]                      \n",
      "                                                                 lstm[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 256)          65792       add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 1652)         424564      dense_1[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 1,813,268\n",
      "Trainable params: 1,813,268\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "qFSPZ9jNuBj6",
    "outputId": "10295074-a4ab-45b0-a618-8bbc0e96a126"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.layers.embeddings.Embedding at 0x7fe324649dd8>"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "hyWoU3vKuBkL"
   },
   "outputs": [],
   "source": [
    "model.layers[2].set_weights([embedding_matrix])\n",
    "model.layers[2].trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "zLMJMNOguBke"
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "jHMZxEvsuBkx"
   },
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "number_pics_per_bath = 3\n",
    "steps = len(train_descriptions)//number_pics_per_bath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "id": "Pj8j4ko_uBlF",
    "outputId": "928e4997-2407-4188-b96d-42be41662f28"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\asus\\music_classification\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000/2000 [==============================] - 480s 237ms/step - loss: 4.5882\n",
      "2000/2000 [==============================] - 596s 298ms/step - loss: 3.4139\n",
      "2000/2000 [==============================] - 624s 312ms/step - loss: 3.1985\n",
      "2000/2000 [==============================] - 614s 307ms/step - loss: 3.0681\n",
      "2000/2000 [==============================] - 540s 270ms/step - loss: 2.9738\n",
      "2000/2000 [==============================] - 508s 254ms/step - loss: 2.9015\n",
      "2000/2000 [==============================] - 520s 260ms/step - loss: 2.8468\n",
      "2000/2000 [==============================] - 531s 265ms/step - loss: 2.7955\n",
      "2000/2000 [==============================] - 540s 270ms/step - loss: 2.7567\n",
      "2000/2000 [==============================] - 541s 271ms/step - loss: 2.7233\n"
     ]
    }
   ],
   "source": [
    "for i in range(epochs):\n",
    "    generator = data_generator(train_descriptions, train_features, wordtoix, max_length, number_pics_per_bath)\n",
    "    model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)\n",
    "    model.save('model_' + str(i) + '.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "aAloWjBouBln"
   },
   "outputs": [],
   "source": [
    "model.optimizer.lr = 0.0001\n",
    "epochs = 10\n",
    "number_pics_per_bath = 6\n",
    "steps = len(train_descriptions)//number_pics_per_bath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "iEQuCCN2uBmV"
   },
   "outputs": [],
   "source": [
    "model.save_weights('model_30.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QCqgNIaFuBmm"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SfEWCx0muBm3"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "2iDlB_FduBnF"
   },
   "outputs": [],
   "source": [
    "model.load_weights('model_30.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rPmctyRMuBnS"
   },
   "outputs": [],
   "source": [
    "images = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rLJiqx4zuBnf"
   },
   "outputs": [],
   "source": [
    "with open(\"encoded_test_images.pkl\", \"rb\") as encoded_pickle:\n",
    "    encoding_test = load(encoded_pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "M21NxpPVuBnw"
   },
   "outputs": [],
   "source": [
    "def greedySearch(photo):\n",
    "    in_text = 'startseq'\n",
    "    for i in range(max_length):\n",
    "        sequence = [wordtoix[w] for w in in_text.split() if w in wordtoix]\n",
    "        sequence = pad_sequences([sequence], maxlen=max_length)\n",
    "        yhat = model.predict([photo,sequence], verbose=0)\n",
    "        yhat = np.argmax(yhat)\n",
    "        word = ixtoword[yhat]\n",
    "        in_text += ' ' + word\n",
    "        if word == 'endseq':\n",
    "            break\n",
    "    final = in_text.split()\n",
    "    final = final[1:-1]\n",
    "    final = ' '.join(final)\n",
    "    return final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "aBh9OQakVFxZ",
    "outputId": "6391c62c-0bf1-4084-bb93-2d0bc2997308"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1523984678_edd68464da.jpg\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "for i in os.listdir('Flicker8k_Dataset/'):\n",
    "  if i in encoding_test.keys():\n",
    "    print(i)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "6J2CnT4ZuBoK",
    "outputId": "81d9a483-9730-4803-cf9a-3da976855c3f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YcZaopBrWgycBF8HmwlJnVCDEyNwJd4QJK525GV2UKB1pgYqwTo0ZZQtD1CFASvlXRBKD6TIUa4VTdvaemCSOMFslJsEiuJNKo1OqMdWKocRh4iBJqyuHvCNLwt4RjEM6al95u0KCv1x45Tvi9ICez1y+fGJpyR477ekV3EmHPRt+DT4/bjx8t1LjE9fDRG7Bd69X2nVnPlx4e39kmpTvXx759Px3xP7MfDC8VmYxtBasHPj4sDCVIx9P78GSvq0UD6650nsnxdA6cz+fOCwzrQVHW4jtmcuXxpdMaMF0Oy1VB7vJYkzHYJ4nJBVXhd25fnrmeZogC6Uoe9u4NmXrwofjTBSjowMj7p3H83WwkTpRXMEmlvnEMgXPrzv7JkhtiE5wP3FSw6thtVAxUjqT1NFSdWfej5RIXBOhcJkcVUeK4LITuVDLjJogahiChNC60nzAk21OOlCiEJEsu9PK+H8hG5GFg8AsM2GCA++koDIR7FQK4QM/Jx1R5cDEVldqCqkKoVRVUgTLAZvuhx3LA15BeuLZOfbESKR85UkuCHfduF7P+O5Mc2UR5WxKqjGfDiy5cNmu+Hqh787+/crfyTPrGlideL2e+fTrZ17Pnfcfv/DxwwOb7nx+uvKy/R0fHu45zBPShaozIYVqlfd3H7ifH7g/nJjrxMu6sW8vXJ5XniM5zCdmqWRdWJZ7ZHbKnty9afzsz/8EKxc8lZBkEvAaJErPYLkLdKnk3KkY1+I8SWe/vtByXJhrXLg0J8qBLAeu0bHdCV95zcbKjtnEBqwSlEh6mdG5s7yt1G6UeWIqBlnxBDQp1nACiaTT6TQ0FclOcwNJTHZW7ZRQugZEQWtHtIAW5NYSOIr1HTB6JJJQKUgKe3bUK6cSSBhXO/E2HE1QT9IASdDERBA78rYrXR1NYZPApDCJknbCArZslCwIE0jHETrOmzihBk7SEaQ76U6jMdWfTuWvIslLKUw/+8BBFQyqQCicbKKUCRHDRVlihm3m2nZ2bayvlV/6FfxCXzfOl50eyfmyEhl4MR63V8QeqHXmmhWAxQqUBRMlskCZ2UuFFF4AIrnmRPhG7U4eC+nJpx5oJleEdpixP2m8O068sCISzFREjM6AFdM6MXXcChaGalJE2ATO/YqkYT4z1Y7qRCvCNYV9vxB74wrc3Z9QN3ZprF4RlE0dm2cOdcJqQTDCKosnO/pb3YlpoyeQSUGRNrGZYGJINjKgaEWzcMhOmiJSqSq4BarJGo3MTg1DMgZ2LYUpnV0mDp6IJiIKUmkxrkFIUjqEKmhQJfGbHmWi0DGqwJ4TJwlQRVxw3Tj5gSyGZqFnp4lCdkwSZcGyEwnFjc5G005V++n8+gfI4X9j2GR8+IsPOBuhhakIoXCwmVKVzIJHp0RBNihUejTKjS5e6Rzv7jnI4caOBkjHxHl7OtImgbuFaVKQzi46MHFbeI1ObCv3mfygwfly5Xp95KpOLckqybLu2KT02FFXrjEo5zxV5JB8zBOeBWQn0HEx0mjaeZOwG+DCQRVFWNSRUCjOVJWFI2mVKQvg2J6EGW+y4LVQEkQKV4WSwTTdQUkCkAAzQQUyhTkFJNFwMgtVkxDQVHKq3GXBBIQyTp9MOkH6EQG6GEpSRUAZsgBRSB09+Y9VXJTwJGanS2XJwiY7GgtnrrQe6GRoCqSTBLMmZCESJi0UFEtFSmA5o9pJXXATpoRdEtDb4GvULEwKooUUIYtQYuGEj5vsJ+KrSHKtyv2fzzggYlS7wU410KykKUphb05pjWQMcq5OBLyLwNVwbRiBaDC+z8RFkDSyKl6NKQIhCZ2gFK4E5/2Z520o3CKCTYQpK9SCh/CSSe4ruiVgpAeRyWKGLDMLMQa0qKzpeASkU2TCRLn3AlOyEdRM0GkcwLpgksyyICqYFFQWrCizJL2MEyFQqgrKxKxJ0YWMYAuIvBBAQSmmKBAk3o2w0YpsYtTooIUZYcpCt6RgkKNt6Daq4xpOAZKCe0dlwnBUnCozmWW8hiSQqBqZgltyyplVGocwutrolYGayg5IN8bVs6FiVKemUXxmTqeJUHJAhBFQDUQrYkmSaDLaLBQRA1Ha7dTUrx0nN1NO7wtNRuVRnakkhhApFHE6UN2YbSFcCBwN2AQgsVBSdhwBGkYSqrgMqWynUn1gtbMdwCpzCjuJ44g6xzDSKqUI4oybSGTIbmWIm0okLhVXSFEOqiy5AEFDIRshAQFGoZQy0AUpHNkxCqFK2o7LkKFOGahNTGp0K2gIiVDjimsBccgEayAHDAExROHSC4pTLJnLhMqMRRvsZRi7Qs1R6VQDSSEmG8N3JilK6U7mdJMUKz1XSlSWhE3jlqijCEFgbuw2ikVm4JQhIUgQgr1M3Ldgvd38mkKxDmlURrtWA0qASUVScANlYPBzgknQbulZErrKGIJFxskCJIwnaT/edH9/fBVJLirMx8qUySENM6OLU8PouUMWlEZWw/uBmDeIiYJyCuUiGxKKyQSygxRSBiGxaVAcWgShBRWjimE2kZIcSyUlqKpID9IE5Yh4w2mUFHomPZXKLcEtMR9qbDGh5IFkJ2TnINDSSTWmunCg0EWwGJizSuAoUY706DQZctiwBBoqowJLDKVgRMdScBEmKywiSHa6FfCAbBhKTgqjNtMnJfdKx2nawBk3Rk+uJEcz1mIIigEXHZR4IKQnlgtk0tRGRaezU2jigLMZaAi7GZIMbb9XukKUEwsrnsLChAN7cTyUmUSlcIgC2ohMsACtHLKwW2NvFZkC70kVJ6OwSTBT8HTshrhkKkVkDNpW+F0Gt68jyQUWqzg7mZVdgkJhlxhGABmTqFrFyk7GguowFbR0jjYx+8IqgWKUrHhxjOBAsllQuyKhqAWq8zAHENh0gISqhss2klkTpDLGOGMJQbPgOk6U6J2LdCqj4lNj4MH9QLfOxI5IxXRCRDAz5tYIMSwVE2jZiBnu2kJYUjFMbwwgBqUzRaGZIDEGx4LhQJVKRW6vVZnTfjvUeRXoymiOFM1xw+/qZO/UKFzZWXplFwdPejqTCW4LmkZmcANpIITUimcfIInMqAZ5gwyFYbZo0TimIiGoHGilkZJMMYF13JVgJHqGESTtVv3tNjckxlQFvKOaBEmYcp9KU2FWpaWjAmKVEDh1o5WR/D8VX0mSK9NygFgG3OSVJKkRNHZcYY6Ca1A5kDZ8e66FpUOzIIuz5ESJikqSOlHS6eLMCX0yEigOXhQRgIrZEFSoFTBBe5J0tFZUJyQGfR8phBSKd1yFE06oMqMIQdeJKI1CwaKMdsIEpDBH0K3iGVQZ1RwmQChlDFepo1oPh05FeqAS1NiHyKko5lCz0AFwRIKDHdHsY/ZQhd4ghBBFxbEsZO5oFlQKYcHsiiFDt5+BAhqGRGAiRCqSAwZFYgyYMnrpCWM30AxmHXoVsQ4xBkUTQUUxHSTRLELKhE+F7h1XpQZECNqUlE4X0HQikmNWnAARXAVEwZRDKE2ghgGB5zhNU5Qp+R0d+deU5OUw+i2ECAURLDspE28wIhud0eu6GDUdU6AkUzIel8N00TSYPEfyioEWusBRIMypYmRA2NCWVxEw5ZgzT2xkuck8JWESsgfSgpJBlz56apRuoCkYEykwUwgVEGfgCTbaHZ2R7EwEXQaioKKYVEgnTNEU5kzcyhjUSlIj2TRZwqALu+TNwrcDiusBwxEtVJTShHbz38xRMFNqCJsWzpHM0XFRSiqmkKGQjuUwjhQXUm5NTDSqDPtcr0Lt5UYQlfH+I+kCBSHa6PWJCZHAgewTao7oDNrHoGjB5AN/r2JM4kQo3hOpEyadblBjIduOljrsd0xISaxf6OHsmaOA4TSUqvC7+pWvJMlhqjMhSVIQ6dQQUioPTKBBdwMc9SAEJG0MJOKDYYzhRyQDTQeCpsFNKkQVo4ugxKgORaBAG9MUTvKaQZGCZCfTSYTNA3UnexDiRBqajmdFo1FRXCoGmAWbBrXbeB/hgy20KyWVyI5poamhptB3EMVwutjwhw47JDVywKYuuP7IMA4/aQZ0QHMHQLOCQpPEY1TogyhHrSBJzYnQHfOJg1bUO5ExXFNdKTlKoUjc8OmAWpBUVGAKYDJq2sC3vYEkIknkhkSHFJquEIWiZWDnWfA0NMbzawguUDM5UnGpIDtNx3fcRJlyqFVUgohxjX706IorEslCJ90JSSR8nJhfe0+OjOlZtJOhWA6zcjMF2UbVY8J7J3RAasM4nGQaCmzmlKwkgmbSwnFVLEaVFSsgOqozhklSc1SjjKC0TvEBH0o4mZ1MwzNZM/FsFBEsg4lRlZIjKTYaj94IhVmU1KT0RAMaDU9IcUyclCQsMWIMUakMZOzHG/XKLolkBXfwIDGKDtVdSCIpaIzEEDOajO/BMkg3JBxMkSgondTkGAUviRBoFiw7koqV0csGw9ycJtQURKeBvZuQLjgJYkwM0ZX6jmgSKHuM55hMEZ2YUBpCVUicPYKQRh3O8xtUmhgMlxZOj9tc0jvZHXenCvRuUJzNdywaJY3EcOmUcSXpvg+D9U/E15HkmfR8JT1RvwxWDiihuBhBQ6LRAHMDGs0K6iN5EIbsMjYKgmegaZCQ2qgyAYqoUUSpUWl56zOjs+LsvkMEPYZDqacNt75AZsHSKarUqWAioAWzTmTBFdAJN6htx0nEBm6fApod6LSEIsmeK+ILoh2RCqocqPQ8s3tHwyB2CoUQHe9ZhFJGmyEitBSyG6E7NQ2Loeqj50AcstEtERPi5n9UZoSBlwND21KMQMfp5gMqteykNrhh0VW5DaPDgT9WEQhugiSYjuFYM4jcx2loMr7PdIpA+orFPOaqXECMagO5mlAsnfTABDI3LG9qAILcb9sTZPABlUKRitNomrctCV85Tp4MsTxso7duSZcB+YXawMBDyBiy06ANXbMkipLZ0XQKQk9o2Qn6SP4o7NqopoATIWw4np0UYc2E7vT9hRWneEMphFakGIec6DhWdDCuxaEIVUZl65pkbKMa+T5OmgwqC00aJoEk9FvjFMWZvNJ0Q1CqdqasOE6G4uloBsZARUpJSpk5iBLuTAEpQsWIMqp8J8BXStpoO3LUuGsExGUwjqYUCZBKT0e0UrRQb6szLIQQ8Gxc+miTRDuaE9sNVdLQsQZERpuoOfy5kRuhlcgbXKtXJAs9g4idTRu1GWLOlMdxU5pQSGab2aONFSIAKXgxIp2dhqQjVqk59DYqlVTFBSZf0CJ4e0W+9kqemUTuZDYC0Gg4FS1CeEIvdNnpJAT0nmxypcSN2paBTswZRDaGkzdpBkUKs48bRNSwTJxKj50WiWdAdHqcx+AqQRYG60rQxSkSKIMql+qkjIEoBUwaKsLOGHZdgyTwOpAWolGzsLDQdSA0aMMCIjd6CEXHqWMSTIwKBQ5pmIC60zWZuD1/MJxIAUFHorBHoipUUbIUVITarmzGwMOtEALmbQyqBkXGjhoj8BJMXXEPdm9cIxBLJhHafOuh50KmY214VkO3YVQWoG+kNJoosxthDQ3G9WiJqBJizAqlBJMc0BLUCOo0UYLBeDN20LSqTH3iKuNkFYzMgZyJJnNWtPjQ02jhd3BBX0eSQ9LljAaQhRaCarD5oKVfpFF6YAqhE1kauNLjDCrUmIZB1h2XcZwWHWRNyaQXgCumlRZJRKPnBR8IGXsKmA0Uw0YvSZFxzKqgkYPMmYLsMnBdvS3iMcVjJxBQAyqLjYsNQZBYNK4Kxky4Y2JYjGNWM2mZFHd2GeL/o8y34VPIEFzHUqQNQVrgsaFTRRM8K66O6cJsQ8xWhNtCoY5lQYRRNDCwjYmKBaQ6ZJDhzGagwmLQIyjpNAevOxknejEMG2i9dkI6JSqJotEHgaXLaBttAqsQnWxGkX2cHDIkGrNVtCQaQohQRIfNsCkZUE1RSbpVMpW9jwVRKo6IYVLRWpmBaBvdbi3UT8RXkeSDqq1jc1YO6e2ocMnOzhLgJTFmNDuCUgxUlqGFyEKqgDk1hJ7BpAUriek4wt13MgKNGDR+DnpYEO6K0VJHjppSEprKkMPujkenuYMaVZXmQ4oASu/7baZIcBmQG4aG4rmxk2goEkHIII3wjrtRA9ySvW/jpKFQaqEySKTMjuUfoAQAACAASURBVMnY/CU6lCCGMpcJ3EhNDKOlMNtEsQml4OJE75gYXR0RoYpRbUJDaB5DcRhyG2ZBMhAZitBjLKzRKQplPt5aRR0wneRtg5mgeaTKTk7CURdUlFlm0gqksdMRF5Q6TpwSnHQGVbI7awRFdVwbBgCwx8qCDdMKoJEUvZ1oJHsCZWzsSgw3w39cWfYT8VUkOUDVGWIFKqkF0UCCW1XqtxVyIH2sCYsEijHLIHhSjE5DJCiyDMzXkpIj8cQEvJEkUyRNymh3DMwMclDvq8MWjshMxIXdBd/HWrk0Z74Nm7Md6HFFNfG4sqoi6WNYDkFiH+0BQQshysydCtYH/n5MH+hQCFN2WgpFgjmSkusgolK5RCdMKSizFOQ2eFZNXAzRQvEhkQXF6aRvY7Ve33/rZGq2Qw4KKmlEu9K8soozyzAqzzqG6jQjLFlk5lBmNgSXZG19AACiTKmkbCz5Y5uhVDWK3VbV0fC2U9XIWNEcCExnH6diGr0P/FwYA6o7NIISA5UpFaZJmHwgR9ErETvpgUjjVfdx03n8/6Mnbzu4gORtlVhXXPqYtk3JNLYYfV5np4RRZSYUsgAIhFEYzNoWHWnKHnHrn4P0oUlunoSOgZBu7NFJgS0E3OjpWO5oVFruvLbLEFzlkPm6dvANF8dc2TMQ32g0AgNXCBm4fDEWnbmXiRlDpHPgQNRBwtCDnjNKp0pSRak3RGQlKZNQZRqEjXITWQ0djFhyWxA2KHDfSXd6u7J6p0cn2dliRsrMTlBLolR2v1V7NVZRJu+kDlgzSFTit3sLJw1a77+V8S4idFF66tgr6UPkFjnWbyiKZkIEko2gYOIDFmXoxMUbhNPUmUUpmnQPjpKoKJpBuoMUVKBkoeXtMb6hDJGcR6cU/Vd7Hf+e+HqS/HZXb75h6UMeCxRi4LXi9HDCdchodWOPQZ073CqGsLXOZb/g3vHeMHGKKXcyUcsw5wI3I63R2ekdZuGWnE6W4RBHCuLCkvWWAIO29hy4bIlKamMxYc3KREEjydJRhMmVOW0YJkjMxpKjpOMMfXxjhUzmzKEK7IGbEpqU1IFqiA/I0pOQwSFcJemRzAihRuboWyXb+J5uA1+zStfEemJTgB4wQDmQcWYLmFK4aKDRb2jV2D6w2Tp03KJ0ESiNRRVNpTN22Ww0MgcsulM4tcJWlInC5gme3HQD1Bsa03J4lYTEbu4iEx1mFnGSoVHZPRDZqGJjG1eM1q0nRFxIF1QNjyH7/an4KpIclN0dT8V82J1aXG8a4UrYPIT+eUBxWl7YPcl2udH0RyYZ0J1HR9ogE/bmaAwZqizCooMsCmP00JkoE7ATkTfRfmEugzSSEGabSekUCio7ocokE7ijpVBsJnEm7/QKS0JqRSLYMTIVbPgQ91jxMFySQ0+6OfShBIxsaCgFiGhkMbIIjlJC2HE2+tB8IIQ5fTQ8KJ1iJ4QNz9F6pOy0aR6wnAWUm51NA9OJAjQ5ojs3w4XR/ZUQR6MPBImkxUBxJtfB+Gqh3AijrXcW7VzruFFUEqnGXMaJuofSJcD3sdlWGnhQZeh0woLex6cotTDLhGC0vrHGSkTA7R4ZyrXh7OoIPs4uDlLZcuj7fyq+kiQfzpWIpMmM9XYbbArdhAlBraAonoL0N7R45Rp+W198QXVGJZluVeiQE1cbtqxSC7NVqh4I6UOnXDtE3LDdIGRDy4LJhEkOUZEE8+54LkOxqAeMnQxQnUkSS6UnYx5wQYqCKq5JRiflZn2j4Djr/npDOI3ohhZDvaOZiDudxlyMaDGILRN6L1zYQMGiI1ohgp1kxqhaaT40OC2FFhsZQxBmmVgvCKOXbVPlIIpEZ5edOk2DbZWGtByfgfE8UyaDRhisbWS7Gcod06Fn99axZMB4gFSYrNCiUwtEE6SUoSpMZaez5so0HBC4wVU6lsmaDdFKxBN7XFl1XCbxof3/URax08Gd7o2ugcpExk83LF9JkieafawDI7FJqTKjTIQOHfUsBawQfWbDkd0xNjZWJBKkUyQxVSYvmFVKKawRTGo0K1xv8OKejXAw7yBJ14mSBU1DTMnYCRrpMRw0riSF1BjmA1ciB9O6ESQ7KUNkFamoVzw6HorJ8JBmOJ6JUDnHjrAz6wxxW+/AUFJKFlZRFh09bYsyjmkaEoEqHLuTohx9+EZ32cjSyDSuLnQPmnS2bFQH7WPWKdqZdyH0zGQFUSPYEZkQ4sYwGnt3pHS8J86VqkNK27JRXWjTQpWgqRBppK9kCmZCxsoaHc3C3mDvV6wUpB5Qdwxj/ZGEz+D/pu5dYm1bs/uu3xjfY8651trndW/d63qlquxUJWXHOA6YvHASJ6IBRER0AjQQIJrQJz266SIh0UOQDgGJBhHCIEgcHClynMhxEsfGr3I9fG/VfZ2zz95rrTnn9xiDxrdsSpBbFeGALlM6Ovuss/c5W3uN+c3vG+P///133en99hQlUttG740dh1ow77QWsZBRydA2Sh9NBBFhpZBpn/yxvt/2agdJ45QsiawQbu0muRkNEgGJY8xtcmTXoXUQTZhXqjdCc4pmMsakAQuRKIJqHAYMk9uezoa8VCFJHHrym8tk7ZHiG+qJoIJ6o4gwe7xZ16C7D6KsDfvaOQwtewFSG37E8bg1XCMwIb6TaBxIiI0efHSna0akgWRiyjxhDGosCFhnr5VKZjInhciiQ/9hcaDnOoUqIA26r0RVMk4Uw6UQEKKH29ZjaMDVhl6I0G+CtDYCAdo2ngA9D6ePF3YZ5KuEs2JYF5IfSDEgXmg+4EHehWRGUMO9jKeANnZVlg6uM5utBJxmQy7R6oa6U1SoUqlEjqojlMBHO9i6cFXh0CrnFFDprEM8wcEnLvrxHET4hBQ5biwdNAaiNboZmoYcUzwgwEbHuhMlsajRJaLBCT6xt4JQEUs3fUOmayaGwCkq3ZwmDl3ZbcU0DAOFGSoJKY7nADuYb3gfK4iYISn/7s1QG8QbXjp6p0dhjpmJA4uOQYbTaVKxuqHe2QRsu5BFSJbIEsmaGGuVkXXCw0w0G1Agz8wxs4yuJkUKhUS3YVaYXQmMLozLDXttQq7DJCHheHsSDSlwF2jeUYZkVVMi+8IUAlkChJnmTifgTbG4kYBmmSa32YIYi0RcQYMPPHWoGGWckWynE8GhlIKqMklg90SpG1Y6e2zMcaH5RpFI6IJRQDJi42YyHxPZTQEzOk6VjibF08Q2dZIpl7rfhGqVNSYiC/5J75OPaA4j4bQecN+HJ9PrTRtye8dxJOzA0ESY3QYaIjQWclyYNZBCIqQJXJAImLNZYWtl7GsdJM44hW5g2tF9JYQ2JpxBCSYkSWjUm+wXdmkohvkgCsQQieKkoCQym6y0biNuRAPJfkdnrewdulRmE2LqJJ1BhRwTxDB43e6DGegVZxrDFRs8QiNQMbayDwmqXpl1wbWAKolE08ysQ/pLdaoPJIXbhIoQVAnMhBDQlEieaDqYMMVnNA4temAYor0L17rdjM2dpHkkVLgxM7YMjUbQMTDrDG+u9+F3NS8U6zQ3tD5y1qExd3MOYUE0MwVHNdNsnCUKK1qNYitVGoGJOR3RIEOH5B0JTrULphOnYCyabgi6f/L1iShyc+exrcxex+NUjFgGviCqYrbTlWE42BRBwTf6zVTsQUmemNNC1MAyH9FhpsNcMT9T65VmjWJODHAMEcS4WKW1Okb19JFrk+dBTuVAlIomJyiENh793TeKKXN3GhHF6L4TiNR+pXlB+47LNNSQVgjmXH2nqzD3oSacLOLNRstUhiIP61gCekS74C7IrRe/tcp2rTRpBDrnDHMUIkoPY9JoAm5C8SveF9wSVTZ6V1LK9N6gJ8xGt0bUcAl0TyBKDqPzEgyyBiQNQcAkTpAxbAo+XDtJErGOQ2kQJ4ehq1FRvDtFOpMmpFy5ysbWHwjNSD7x2lYOecIThJgwnHV/oMojtXWcfbyvWnGLdMtDgy/G0SORCWIkSydOwyf8cdcnosgFR/p5jNY9kSVjQdj7yoaR6TSbcCt0U7rVMeXMoCGQdRS16VDldWvs9NFTboXaVjYv1HqlCxw0oNYoOsT4GkFswzzhuiCSx4FURkJDV7u1e50rBvWKoFifUIUzg6paZOxtRQX3PEhWoliY6FRym7jUlSKdY1BiHgOqnYp3MDEkpCHAunVSsI5Xp3hla5UmY24Q8wFTo6ZEj87uMsRNPiQMqFMdujfMfBSkZ9Rg64UeO0IBHzQClUjUjKpySIJqxl15qssoalupdmV3R1Ikth1QcgzUOowpwx/aySkNkliH1S6EVMkC2gwzw2gkzax+hWagM7WCmyN9FDeSUBLZB/Iiqo1tkI+iTWmhch1eT18/+VRbVNlu6r1MROJwwVSthOZjRBw2lIkiO5mMJyFrIKaA0iju1LKx6ZXWZ5DG7o3SroTuY0iDcZADmZuPswsRoarQJaM9D9FVz0SM3RqUTmWj9YZ44yqGaGeuRrRET+0WBePDoRQh9EiQsUURViBylERNSosN6Q1nhA80L7Q6VsAiikzKwZRrvwyIkiv4zrbveG10F9LkuDbAufTCVipTmMYGPFRoAy1hmm+KScdKQrlpVLzTW2W3wi7GE8/EIEM+znBJTb0PbkroQ9BWV7qt9AGNw+RACpnSxg0hfXSPrDvIxqCjFJJPY3ukK9vN/R99GVp1d1xHU6HHzmSRPT7jKZEWlRgGOFQRulaiQQud4owJaXOoO95H6tzHXZ+IIleU6AeETgwJWMZUsQag0FImCXQzFg94GAIpDQNo2UywvlKLISoUdUwq3hrBGqoTISlP+3Hg28wIfYyvs0xMQL2lk0XJIwVBhF5BTKA0EKEaLESyZyQE5piHhFR3YleuvjM1JUoENZYB7iMFw6KTMUIJQ/KrxhhzDBlwN0h5Ies83sBQhhDMxnYl93hT4m2oC9UHFs3c0NY51w0wDjXTehkT0jgcRbYb3Fqie6/gRu0r6s4xnogpEOPCIQRaupG+fEx2t17xVnHbafXGPylGWG5oN2ekQGjAWuXeVyjOSSZ6MDYvTJqIYeHuxnTUEEgyE/rC5hutdxKdEgsHmXAKkYT1NiamUVjbTtOGm2DN2duGMIZ0s47Imo+7PhlFLsqT9Ayzdbh3dCSWqXSEmXwjaFUZjm7zjuhOo7P0iOoQF+3ZUQKucZgOvCEUNC3Mg63FbtvwI3ohi4KO8KhFlWr1NgHsNB2tPmJkBvAh5W0xkX2IkTQEEgNUue9nqIHOjltFxNnqNoA4TfDQSSGRUiaKju1QbXgfFFdNEy/SiXg4EmNhj6PbMLdnSBN6KZhVaq8UjC30MYDpDZFM8LEnRuKwUHsYDJXaaK2wm7Ex9Ppr7eQAOR6YmZnzRNSZacrMQXHp1Hqh+EgBoRl971Q6rYx5QVs3Vi+DQEsj5QOtN9yH0WGbIhMQGTY390AlkOuGxYSpIK5MlhFvbL1B7YOHQ+SRnXBDV/Rto/XCVTKqaTQq3KhtI0pk9fX3tpKLyH8B/HngfXf/Q7fXXgD/DfBF4OvAX3T3VzI8SP8p8K8CV+Dfc/df+L7/B0KMNqaD6uSbF9BlAC0nEtzwBuZQfMOrM5sTcibEiFomi9yCUQNuvyPCOt7aZzbc7q1zqVdydyxNxDSscl3igPTEdXw/MvyPAUiHmc1uG2VJpBsCQZGhMuxj4OSqXLzRxBDbcVeaTWQZdq2ow0GfVWjWIUAJwokDzAdCSMzzRJ6cu+kJHhLaD4RyxMpOa4Wtj9U11YKkiWAMalc36B3TxuRx/BwczBt7S1QzmgDWiLLgVLwHWoLSFJmhex9+0p6p9cLed86lUetrenGa72Q50E3Zy05HKbKRVYlmkDIOZLkZi+02XPIzJgnxwNULba2EeiXoiuSM9cZuG1tb2coOJmxUMpGcbGjzLbO6McvG1Yd411ukqA/G0veAUvzTrOT/JfCfAX/lu177S8Bfd/e/LCJ/6fbn/xj4V4Av3379UeA/5/ukMcOQAqeU6ftK6B3ilaTT/5kIHG7OcJlHl6N3Nq6YOEGFGBKexs3iTUcB0W4tso7aQMaJGzlUNuu4hJtpYBCr3Asxg/nYk4sM2WG/qbiJjdYMb41Zppvqzdh6RdpGNWfr+9Cd6Bj7p3DrfccZl3DL+VSaBJBO885BEh4zMYLcRvu9CZoDgTjkDWl0lKqDaiBEIaVI0omsHe2Rdb9QxZFuxBDYaweUkgIhZqRFpt5AGtaHha412PYzoW0kV+6m5+MGlpE5VFujtUatnbVuEAJZjBQTzSeKbdQCHuNYqtygORfreN+Ziag2uoyDdeNKb43WdtQyva3ompklcElXttY42kwUpzF69A3nFJ/StHHpK/vamGgDRpomJIbBTf8e1/ctcnf/WRH54v/l5b8A/Jnbx/8V8DdvRf4XgL/i46j7cyLyTEQ+7e7f/j5lziwLm26EJlgZUKCQxnpZfGDhQoQQhwzU2aBlXHZWvWEZXKm9oX0dzHA6xYXZC8IIuYp6YMoZ6xtBZ4gBDdB0p9KgKJlOMfC2gijVB7O79YrUzJaESWfwhtygOHS7kS5kPLa1kcJMDjMhQPed1sPo7fvKVjeaXUGVoEcO9YQFo24biyXMhPFWPxC4EkzGSnszaAeGwjGJ4BFsdaxV9j4S2Yo1cpqIEolxRqeK24HWz0w41hNrqJRtBb9wccPqGZ2f8CweiB4QGSulBSHOw2hsc8LFOWog2swhn2gCOSZEnYJz//CS4jtP0wGohNDp+oD1iZ2C2EorhdIS5oFpTiQdRvGiBcj0Yqw88rTfccrKFiberMb70yNeYVJIwclxoXz8Iv5PV+Qfc739XYX7HeDt28efBb71XZ/327fX/m9F/t2x42+8+QJFmFjY9Io0YbcyHOA1DISM7oRNCXnkzCMT1ZzqGyqFlO+wJjdPpo6hjRiRMc1LDgRBbMg5xRdEEt0AHUo4LNMF+k2Psbd6s3NF3IRaw2hvdgE1VIVIZk9CjMLSjN4bRVbEdLQFtQ9jRBeutuJmdI3srbDVlSwwG9jUaEGYwwQFtu5s+4XijRzvGIjTYcx2NUQjpleSC20vXK9XdttpVqmtYZKG0C13XG4A1FBwq9iNFXlwoc1O6RWlcLZ7DrWzY8zhSNDAcU7ENBF7odSdHCtRE3NYODKhPXDxnaiZEioqmefTG6z9zISgOd4mqiPmJbuwhTCM0qWz14KkHTwTbqjnYBUPO6UVHsKZF/KEKBN7TtxpJMjKKmP7ue+FWob69J91kf/u5e4u8r1spB/7db8bO/7FH/yCO4JLRHvGrA/jqjnYiPWopVClEW1gmAmJ5hXaleARtYBJRglEzbj0cUDtA+PcpCJNUAvMNuO9s3UhTaNrI57orSJ9Qk1Qi8xxhqaoTJS+k2LG2+AObm5kdyQGtA6JblRFa2WXfWCP604MQpSZKmdKb6O155UUOqHfEbTeuC020qdwmlSCKdUYhmx7BGvsdcQbZgHTyDwd0BaptdKbUcvGHpzSd2JuFO8DVF83XBK7VrIlmo+zxNZ3xBuqCfqOhkhnH/EqVii+jyUiKNONI65qRN0IsowpZMoc6Ox9RcOQLkyHRrDjMIB7YTPBLBDDxKzO0RasF0odKSE9dOYeyE3Glssa2jeyXUml8aG+IpIJWpmkY9GIvSE2k2weGnf+2U883/udbYiIfBp4//b6O8Dnv+vzPnd77Xtebo5tjvooKI0DVeYObh21QtRE6Rvex8oc+mD8PdYyHlu9kkMaxKkUCQ5t71grhDARdyXExOoNNAzOnjvbXth15cgR6jACV+1EH/JYpowEsC6UrdHZ0K5jgimNWIbbEJ8Ikihy8x56GSgKM4wr8fYmnG1DbSgrgyaWpOPAa0OL0WIm3TpKdwRadPa6cq1XtvVCLYZm5zDdMQWl3OBCFhpVV9a+0cyQmnkIF3QTaq8gkF1hPlHaFe2F3gtBIqITWZ4NE7hm3Jyzn2l1w+Jox2rrrDbQdRaHJp4GOVzHtqpv7MV5kT/HXX7OB+UDlI6IM1XIJLYozAiqB3TubC7QzqSQiDkzCAVn1t5ofaQLBQJbXwltG55TGzd0SjMnnTn4HTUOns4/6yL/a8C/C/zl2+///Xe9/h+JyF9lHDhff//9+A1JYc6UMlnSSBKwMczpZaQ7aNBh2u2M7YYEYj0QtsqmSgiZK41gRlpGx6TufdwU9gDAlA7DFDvPaB+j74IRSuKCcZJl2O9ulCuIJBHSzXAg1dmsUftKDxltxqXvZIMY4hhnS6OVoQtvwN47z+RIiZVWO9UKi2UuvTARCDWSsw7jQJyZ7EDSfHPKdM61Uc25bo1z2ehrYbIEoTLVSrBKM2dtV/a9ca4rVXdqP+ES2Vplbw9kTXz+7is8fOPE6XPf4mIfDuWiZ4I5KR7BAu6Bax2DnbU2KAVlZ6XQbehkst0xYSjXIc7ygrQOvvDB1x5ZtzN/5Kc+zUf7A1kiEjeqbBxv5pOJhT0IkleWuFDDYFYGG6ybzHW0H3snaodWEFmQXShN2XdhiZF9cg6HTEwjZe7/cZGLyH/NOGS+KSK/Dfwnt+L+b0XkPwC+AfzF26f/j4z24W8wWoj//vf7929ljqowhxmTQAyR2qFu45G26EieMBm6Fq82UG5F0Z45MY38HTcMQ7YNlUQ02HthL8Oi5UXI80SS8w3aaVjfiJqZ9cAcRrRf7/mm/y6EIARhWNvygvsw+DZg5wH/HXNt2Km+I2SSjtaZS7/l4mwkmzjGGe877jsJG4ltZNDh8a+3tau781gGrm61Si8N1UgMB2IG10iSyqU+YD5wFc0Kl2LcbxvuA16krfLQKo/lwmeefIZ3vhb5ez//M/yJf+1L5M811uvK1oVDgMWPzPFALzuv+5mtVy6tMDUbOLi28thWqhSeTcqsJ9Zm9P1hAD1bJhD4hz/3twmW+JEf/WEOz5Vr+YimnRyGTS2FGY+ZbIHWdlYvY2HzoQgXC5zCU3J4Qe8bm9+DNaxnmjizjNvEemN3Z9NG94b+XlSI7v5vf8xf/bl/wuc68B/+0xX2d30dTvHGbjspDu3GVgutVNpeeVTnuUxjH1l3trJh1qntxhFMRoods4KGNqJH4jIiSnSh985l3yi98lQ2gr4gpkKSjkTFujFHmLMwxeE2xw2Rieob9CEJABmDkyBY60yycKWBBSCQfKgKQ8hsopR6Rr2ReDIsbiosHui5gR7IOjHFCbON6jvdjC3CWo19v7L3M41MCqC5cwpKzzNRt/F9u7C7ITYOya84o5qQ1qBlWg1o64Sa0Pff5m/93N9gfXjgnV95wpc++5zeHW87XjeYN5rteO9c+yOv+065Gg99aPFtM1oBD8YWG552ggyz8ew3l9AK5/c+IpxO/G//88/zp/+NHwGLXGyoOw+S0bigMsgGPQjJE1WM3Do9Os9CgDSzuHO1SOoLtd3jcYN4ILnQl0DUhUKl6CNT4JNvmsCd0AOSRx5PbQXtnWzO2XfSDlVHW28vZYS49o1SC5mA1ARzIHgjxcASEzEMU0HgSErCtDlbrUgMwAW1w1AOuoztgqyojBX1mIfX0LWyWWaro9DVAyko1Xeab4iPwI/aCt2FQ77jbjoiDCnA2sYI3dKHHO0FlofDqNuVoJ0UGBDQCLVt7GHHgg2TxlLYayNKwJvRtFBjRbpTJSEqWNnYQiX2Kw9SCHnsWS0Flg5bvKC98rnpS/z9/+XXebw/Y+WB77x7z5ftDU7R0SjMDLFbp7Fj9OCkqyMqY1umjvgTRuKgMkch+E5UyNlYCGgKfPDByuty5cmeeOdbv8Uv/707fviPveCoL1B1Wm+kJrRY6F7GYR446HOmg6AhsYRCFeWxrDSpxOh4WMAru+8omSpOvOG6e994kI59D7/+J6LIVXSAgH6X/DSwYrHASSOVlb1fhpQ0Db8iuZEPkdAE9TTiyOPMISVO80JkGRhlTRyWZyzTzGW9Ys0gBLQbrk9QHRO21AWzHQ+R1SNLUKIb0hpy45O3vuMduhZq3yh1pZaA9MoUF+akTJNTrFHLTkNoOuJLqhYaO3sY6AZvhV02TNttOzB422qNkDpqiefhCHGihUopfZgivFN9B98hBIRIjkfe1COr75SpYdIJciW7Eln4tZ99xbvf/hpaCr061/3KXVpuQ5xBtK2tc7ENE2EKR9LxCXkJtDyNFmeHIsaCUctgHnpykixMcmCKytf+4TtYacS7QKXyrW+9zx/5U28hIY2WbmgjQS80zqHxPB85ygsEH1i7EEm6s7WVx3YhEZnyCbNCqS/HOUkdrcNi10zYfOCnP/EqRFG/GQkaKsouFS8FC45EZ84OoY+QJQKHcCJbJXkgSebgC3Bj7eXAkhJicTzuZfD2osEpHSh+pfWJJo6I0SSNrUdwQjOKGqFfyDFRu9FrYdt3qnVMGhqcqIkmSmLCpTNPR5b5QArK6o/UtoPsLPOCiJPjuFEbHRMwC6hEqGOrhTecxFavrLxi6jPJEqdwImJsMs4lIn3k0hdhxUF2pjgz6YJFRWVhmjawPqaSBA75wN/+5i+PJI8YeHj9yOVxZ7q1YncBmlKksfUzmiJUY46JZZk5HJ8PtaRtPKkXTGFeoMtE9DxEaK5onnn327+I5sizuxNbUx7ee4nXKyEnPCRog9jVU+Dt9FmSZxbPbK0Mvbydx9S4r2O7GPvN3meU4CPNzxoxQnHo+xUJB7RH/HsMhD4RRa4IaGHrA+NWWuFS16Es005ICQ2VpJkpLkQCuVV6Ne6m5xxCpmkcj7HkQxdtQt4aqzWsjSlgM6F6GrRZD5TW2H3sLWud2H0H25nljuSBJ0Bl5L1v5cLuOylPBFXmcKTmypRAs9K8UzHuLx9AbGi8xbEQ6A5VKlfpRDI15gG6xxAfB+piO8WM2IXr7szAY7giIeJsPIaBgyi181Afkd1p0vHsvJcL2U7kKEyeOM5P8OjEcKLWwP39lkKGmQAAIABJREFUS37g9AabXVjPM/ePj7y87JyOiqSZpkZPTtYTczwSxDnE0SmK4mgKWIVr6sRYB9hIhdkDdnN1Xe5fcf/elXka70WYlA9e3vOqrKT4GomJjt8cPkdqb8xNh/a/XDjHlcf2wBQE9UJh56E1ogdyK3h7gKxsIsSQuQsvEH3Cw75SxW5npn/y9YkoclfAhXJL/+1WyGmm0YgqgwIbZw4ciPEwMM3hGZt0xDMWBgBS6UgYk8hDimh8AevG5iv0DQ2KaMHLlVKdjYpbZ5aE3ayx5sI87SzpjkNMZJs5h4xfYb2u1LbSYyXLQpURXqIKtVQepdK8kE05OPS+8rIWhEg4ZHKYaKZ0W3lthTXCoU2gzrWs9H3k84hUthDYqLCfefCdQ5g5zPBMM5VEFSh95/56IbYjd94QsZEsbQZzgdxp18D+eOZBMpM2DqenXLcz3/iN9/jyT7wxzMByICrUNLHc5hRJBRjpdL1Wiu90u2AsHEIEFzbf2LaNncKv/50H9s1JKbF/9IqaRoLGB68f6OUjppTwlFniaTDom/LYCm/6gSVkSlx55CMuYsxduDTnXPZxmHcjOKzpgkkh6wue5nxjoFdquQwt/8dcn4wiZ2Q7Oh13J+QjSzihcWBnO1eEjFgeIPw009uRJA1NA1RT+g5A23dKTsxZiDEiWTnIhKSJtRdkH8lmZyqt7xxkIucMGFErIrBMg1ny6G14F6MRl4UsJ+r2emxTbOwji3fW8wNFO1UTiUjZBvP70nbO60YjctRMiMbed16Xe4Sd2UdvWSt4C0xhZBm11tm5p3dhFcH2jXi8cirPuMoj2TsiiZYCgTtgHTgKc9Arj3tjap0trJzKHSLCXtdhUbNGyQu/9Y8f+f1/9NMcffxMI5HdGyZnqkQaQqg6PJwMfHJvgUMzQo7svbG3xmV7wNYTH/7KlXg4oNd1tHHLMDq4b5wfdurk6KyEVBES13ZGWuHSGy0d2Oo9r/o9rRmtFx7LRqqZk98h1vBJ0DwMGPdc0etLlvkpWzCq6/8PIg6B5mUQniQNQk0KLOkw4JQe2Kxi3thqpzRDwmBdBxpVnb1VLud72vYemzyhLCdkyXSfOEiGOSJ1qBqrdbQXEk5KE0im9Z0UDixJiWSuZWe77uy2seRM8YDrNDAQPs4HJvNAu1knhCPP4lilX9YPKC0gJIIGWm/oWtmjs5YrSMNlw/sOMfAsPkGnwMPW2W3lsV5YL42rdOYpoaFxXQuv+mt0vjJZ5G75FG/aM7ob1xpxHqnitD7CclUbKkNrblpwmTERWgy05qyvC+9/vfKDP3gAOr3tdFPO/YJjRBWyH5gkcomVJ5J4I7w50BZbJbjxUF7T6srLX0qDxtuhded+35jvTojegggs4jUwxXl0a6RxCkcExcPCy3rluj8QYqW3wPnBaDUw+RPO2oix4WSe5efMaedIY+0rISSyLvQ8Zg0fd30iityArXcOJizpSMwH4pQRq5SysgYjSmNvF1Cl1Qu2V1QbYQmcW+V83Xh8vGffPuLIR1zap5CeOOpCn59wrAs9OkmcSY5MBmrG5E6VFY0TIR6Jc2TfO7U/0C1QamH1AmFmJpHSHbudaTETdaQodx+2urvpKef+kl5ec6GMsb0vnHsnmEE7E9LCp9Ob1LBCe+R5OnBKR4IoYlde0Tj4EYk7B1fmWTE21nVlazv1deEzy5EncmQOM/d2JQSlSaURqdLITMSgSDwwpxPz4RnahUDj2sYQqmvgl/7GO7z9xZlaX5E8s/VGFSdIw32m+gM7ijbD4x0HEhac1TvnemUiMte3+KVfe4f1Anu/sG47UzhRXXh6t3A6BdYbuzxQWfsrkkbm6QRp4lw6rBeICdVhtj6eToRWSH7A9UrIC/OSOU7PucsdtQ03JepM7Tbw2d/j+mQUee+cz2c8n0g5kOLI+Nl8576/guaD9xHzQISpY3WjaKVeV86+sl5XzqWi7mg6cG+NsG2QymB9Z2fyzDQdyNMd4plvP77L6/Y4/IgxMMd5oBRyY9tHetthmtnbleIb5sYSIs5M942JhRQD3gedyuyKWAEipzz8oybGLAtTg+ILPU+EcHdLXAuE9BbLfKD5BSuVu3TiyRH2dJPx9sK1QJVCLZE7fcYdz5jaM8SHQdlduATw/pLcBUmNPSWOGunAYfkU96/fw915+fCSfHrKum30svPeOxfCmx+RYyCEA31tiGZcG6YZWsWmSHFlTVDqykf2msfWyS1yev1F0HfY1vd4ff+K3ivzHFBRDocnWH4gps7kAa87Mzuv94+4lFcEfTaeaPMT7tzJ+RnFItHuqfNEUKN5YsmRPM0kHZqhyU4jj9Vhq1cu+2VgOj7m+kQUOS5oX/Auo11Fp/WG1cq2ntnVyKpoGuzv5IGrRrxHem/UemVtjQOZu/kJx/wmc4h4bcOh3kbmwx5HAnMyxcPKWi9scmbySIwnHst7ZHk6cNApEqOROBLnmX27p5tSgJ1Kq4Vzv/BmDGQPWLlQeicAb+RnxDDwDaVuaM/UNHAb1itrf42ECzFNyAFqD3TCgJf2zOl0GuTa9orHtvPokZ3Ik+OJN9JTDmkhTJnz9hqLkTVWFgLBI6tWLMLsga0NyH1IgaxO9DQ0UGshHq688am3eP0rTv5jG5k7vvz8c/zYP/dn+ANf+Cq2jcCsmATNwuPlnl/5x7/IBx/8KpfLhW9fHvkX3/zT/K2f+985Hhe+/viKWlea6RCfpdHSrP093I6cMabqPLSVjZ0u8NRfQ56JAab4JnNY0LJx9/xtFONxf5dVnWYV6QW1jWKF1MGaMKfExVayG1F+D9qV/y8uVTge06Ba0WhrI2CYw0HewGoZQqo6BJUXKbhAdCX5c5o5huCLcoxPOaZMlpmIY8ZIGWudS7vw0CpLmjnvr8c2zjoXbRz3xiWcWft5SFh1ovsBwgB4NgnscuVy7RTZeL2/4o05DW+hV9re8FBIKaAxk/KR5I1OZJdHLE/UrVJKZZVHZpyoC90mihjXvXBtlcRG5gCSeCXCqo0eGvmYmUXJS4agmBrL6cS6P3C0Su0bNW20PrItLc40nKpOTo0pRM7nM5XK2298mrc+8zl+7A/+EPX8mh978i9j9Rk/+Yf+LF/5Q19AwqCWOdwSOZy3/G3e+1onPPkUX3h25ieuH9GuCz/14z/M+aPP8q2vv8ury69T3TlvG5/KJ04HBZ5zLg3phdfcE7WT0kJQx0NjljA4794R2xHZqR5veUaB7CNNr9SNKsOVL3vljfQpXjej0FG98r3U3p+MIhd4chhuENEx9YsiKIrpQuqGNRA/Y0FJMZNzwm2A3Q/cMekBp5B9eAkDiicZ9CfrNE10c0p3rD0QaTxPT6HCh30jiBDc8f2RrRoSBbHKQzIOMlMNHmxlElhbI9iMdfignAdxVYVZCltxxAJn66R0oAMlJUp5RD0RPfAkRDyMNOJ1XbFQqf3CVa600Kg5Yz3wys5UX5EMGgaSzvWRex3nikUrssAkgWqRtJ9o9UrrnUZh1kCQytufe8L9tz+iuRHDzNtvfYY/8EM/wpe+8Gk+/cZz+pr44o//CD/41S8Mgpj7QHPASJMD3Dsfngs//dN/l+Mx8IU3ToS28QOffoPj3cJP/ks/wf1Pv8vjWkkB7usjX/3yF1CZSaGy+kqrNhoK+UichbkniiTkGil6oSZl7R0rH3LphaUvrFaQKrR0ZUkn6EKUA+gdd7Hy3voSq4VuH89D/EQUueA888yuldoTVRreIr13zuWRbR0JCthK7J3pyQuyCqWPR9QxHtm94ASmGAkqEG/gHgGJaThdUJpWrD9wkJncJ1qMsL4mmHH2SvGEtQzd2cpG5sq2PIPQB6nVd2ZZSMunWOITiJVuD0wulAIlVLrthLZStbFMC4smTIz9WqhpZQkzu1zYHGYZOTmPXLj2R6I01t2pKK0Z4o0wP+HpdKS2wsP2in2/5/2YOckTnqUnTHpAozJPM1O441qvdM0sIQPC57504B/97X+EAl/5oR/hx//wv8CbdyeCOS/POz/wB/8gP/YTXyGN/O5b/s7NTI7fktWUH/riW9wd73j56iPq/cobzwIfvLrn2dM3+fzv+2F+6icv/K8/89dpdeONJdPPwrP5GU9i56P6wFUvlB7pKLlnqgfumuLBEZ3ptlDsNd4WYjUuViilE3anhwvkSPTIYX5GlswerswiXDn+no3M/+9fzgiH2pVilatXMCX4SmvbGFtboHrF9YLZPaVlzmUMJiZJtBbY9MpVO4tEFhVCmNjtggXHQiXrhCLsBHZrA4TpwmHONNu4azOPMQwUXB85PWs9g3fidCIQMBVUO1NyNELXRsc5ygypYTIg8quvzNoQtyEPtchjWtHaeKU70isBIYWF7p3NG7Utw09pgljgIJE4v8Ec3xxQy/rAVF5T7Bm6lWGwXg/saWdTRY/GHJ6Q+4g2dxKncMcv/uo3+MLbn+cqnT/5J/8sSyy8//JDni1v84u/+tv8+T/8J0ACIrci/92C8RtleJyF3n3vQ775zd+ktpWXG+jyNh++8x5vvrnyA2+94Pd/+atIMX75H/x9fvwn/hjn14UXdqDlChQWnlNt8NytKgcRWtkI3WhxwFp7SWBHsgO2Y9FI3rhaZW/v0/0FRQL37cpMZYozz6e3CPrxpfyJKHJz53G7p9aZh9bZinGQxCGnAf/Rgkwzj7Jw8Qc6F3xbudYdkRMtVK71YThiJiOcEknvCCFhFLbeMLuQ98qGYaVQ6iPimbVdKPJAjB1PE0vLrKJMBKZjJtcTu1ViN4J0TCvXvnGIJ0I0ruwsJNZcOYQZozL1TC4zm3RK25lYkH5AyoVqCbxQTCBVFu10h7L7wFeHjHKAULE4RjEeB7+8hjMSlFkbd/acWZ5z1Oc81CvOSqzOo+2U/QEPxil/kfvfiHzli/88v/Dhz/Ln/vifY9LOu+99xGdf3PGPvvYdfu2d9/nJ8z3XfRxSVeS7F/KBT67Ot18Z/9NP/03e/fpv8Nbbz3l8/ci3v+186sUd737nHZ4lId4tfOVHv0pU+M1v/Baa7/iZ/+5D/tS//mX6ekVj4Hm8Y4sre18JnNhkZyvKkTFB8/hqBCGQxgHWKzJPqN7dNGkTZiuXvbG2naMGjDT4mB9zfUKKvLPWHfdGY/DI5zRxTHFErFhibzubPQBC85GOoN6p1tlZ2Xy9wWwifr2yzU95uhxBX2DbGW8yYsr2Cw+XezYUbWcaxlkr7js5tBEINR0RVZIeCNOJRQqtFFq/YFthmhdSeELShG/CR/0VJ6DPYwUO+W3UhH1/YK0X5nzAxdn1SiiBWDNNruw9sIfC4pHz9YH+uFKfHjimMx1hr4VDOrLuFRUnhkrV1yTicL/HmarLQAZJZHcjlIauhbsnJ771C6/5H/7qX+P56QV/9qd+imcH5Zvf/g7Pp8irS+WXfv03iLHziz/38/zoj36Z/IUXI95cfycxUKgdSnXef+8Vv/rLf59vfOcbw444LawvH+HZU54uM+998C7P+3OenSa+/EOf54PzSz787W/yy//4ni/98YnH9B4zJ4Y4vlFqx+QMEoixEOSKx0ZsZ/BMno9MfWL2J/T4giNH7qaZh/IB37EHtnZP2HeqTlQLn3wVIgYLM56cEDoS4G6CRWF3HzLT1BBgtol5OqFeuI8bASNMhjVl9hOTTGgYLv5LTRymO944foG6V7qt+O4EudL29wnTzF28Y7Ijq12QeiZMI3y2t4rFhVlnLBn79Aj7hahCYkh7e5soZePlds99OZPPxpQ+NVqILOyPl6GmfNKwUKFV9rKNKBC/gmeOcaHFhan+AO+t3+R+f8Vd7mRVVnPupdBaRFPncCjMCkEbxSv7FFlyJKYZpXE43dFx9pqBmXRw7o4nvvT7Ps/nP/sm337nPdq2oW99hq//xtcp60vuzzt/5+/+Lf7Nf+ffotqwIrqOppJ1WA0eV+ObX3+Xr//m13l4eMmvlZ27wxu8+eLI/O7MF3/fW7z7/rvY+ZG7Wdm3R770xjP2y5mvfj7Cch5AVSkc83NE7njgQveVHE8kjClNqBhdAi3AaTkSPUI5cqzP+NT8goXKojtWr+xTAjLn0vHcPvlUW1Xl/2DuzWKuX8/7rOuZ/uMa3/Gb9+xt7709xG5iO0mT1ElrhagNGVRCS0gDNCIVB0jAQVErhHqAOEHiNKICRKMKShpImxZRtXHcTI7txI6HvbO9529/wzuu6T8+IwdrFypEI4SNtJ+jpfWerXVrvff/fu7fdc2ziiQzbOqBhH5PLz2loJ8qhJUYk8h0RZnN6GWP7Ha0IeHl/sKg9hPmZkamFC0dMUQ0OUKYPcrCRUbR7RXZQRCjJGhQqaDyiSQzKHIkA+AoGBjjhnbY4XEUsSZ6xSAsxo5UZt/3Nn3AjI4yL5mIHDSMqcOkQFQl0SeE1BRiQl92+LFFM4CKtDGQk7gxPyGpyHl/n2D2iOdZviAEyXbY4bo9/6R3AmUArUkyIHEMAspCYkUgGsFgBNE6Dp8/4N69O3z0g0/RXG1Yj5anjk55uFph+yva9YpN13CYTum7nq5LaBJ5JtESokusB0fbed584xXun7+BcHuwU24MF5cOoQpUXXD75Abtasejqw2ib7h7XLE9OuHup3JS5snUhCFvUXnNRJ4Q04Sz7mVyNSKlolOaIhXIImcaHKXa81R8tCgGhnBOQuGkR2c1JlsQYktmNCnLkO/3nlwI+R4wyJE5SHEgoySXGVLkpOTwuaRSCwpRkcmSMpWM9ZRitAzOkmJPWdRUFHvBdpDEoIhjIqa9fSyKhDcCmxQ+GvJR0YQWY0YKpRG6JklH0C3EHmVq9BgwvWeWZuSyYOcj525FkzJuTae4UGMwQE1RHFPmM7yw+NCQVRJCgZQDvdvLpGayxM4TY3SkQTGGDUYYVJVRyZJDVVNlJfPqHmVRYsOayz5DNLBNkkKPKCUwRaTICnTKsQSi6wiiZwiWZSlRWjERlhdPjhBDw847jmY1F6vHvPH2A9549z5D59h2G7q+ZbNruTi/Bm+p64IyyxgHx9W2YehHvvzl32cYdsD+5rLuMtZpx+AdWklSd8iTT96i2T1m6EbeebDi3uwWB4clTb7DR4kPI7t+h84mqBRIZEiXmOkp4yjohUa4jok54cjMiDJjZR9xwSWXNuMkP8Uqs7djewl6glYVOf79L8aSCVa7DiGgygy1LiEonPKouKdCObUjCA1FhvUjhRYUKeKEwhlNKQ7Izd6dab1DCEke9zJYpfbSLQSoFJlPFtTVhBQbfEpkZUku6v2UR3qC248wRxQFhjyVFLLkqJyzKTzjtmMbLLuup/U7ggvUxYw+CmK7wqYRLRMLNUdlkt3wiJUdCMKzKE+ZyFM0FSJfIaLAywEhBpQckaUk1wsKNUfrjABMTImaZCR/js0sKmUYLdCywEdHDI5GjIi+Q2nQWpDLHCE9brLl7MpydPcWQxd4/XHL/XfPeHz+iIgnJMuDB/f5/Of/Od/76Y8xrUuuriOHdcV2dGy2PY9XV7z11pv7VuY9/+Z5v0arjNlkyc3bH+DoYMLl5X2OFnMuhrdZNRt205Ek74Cu2NgtzTiQjxtS2VGYGffkEVZHQkgUIVB5jddTKlUz54QkBsrshCM5wWmNKUpsyvHj/kJOmBllMowO/iTyz/uiyBGKMO5TQbPpnJQZ1k2Ha3bgLevUE6MnlyV9uWGSz8BLhAhkqiBDQ66o3tuwc/2+py1kTjQBLQQpJJrk0FXGSXmMUI7kpnShQWdzpM7omg1pTMzkElPVVLqkCGm/BqwNRVkh6LmtbmN2O2LaUYeWZTXDa0O7S6zsSBV7JqrAZwGbNQyjpw8dAcWxKaFYUGeGZPeTGp96Ih6dOSZR74PdfkfoM3zaIIHOB1xURNxeIDXAKm7RQjEGuR9zCtD5SK4jvQYvK+78wBGv/N0zGEY2acbaJZwuqIolu2GNTHB1fcmv/69/j6fvneAPjxj6hr4qWG23tKPna6+8w9uvv05KDsiQaLQqmc2OCN7xlW98ifnhAXfmGe9ePOAASRstR5+5hxVgrIBQo4UjZYJVXHPTTyn0AXUmGPyIHTboAEYtQWTs7BXO7vF4dyY3GIxmRLBJHfI9rntOS0uiVOn/lBD/P533RZGnFMmyGXVWoiixSdL4gaYZ6dyGkUAhDUJHorDkylOoCqk1pdB0KuB9j7T7K+9MCabC4GQk+kCtNMFEMptwMjKmHvWeNHWZHVKqnIuxoek2yCQ5nh5wZ3qEEgq8o42RNgWS3OcfJ9mOscgYnUPke89obwXXuzV2BKMqBikIvgcr8cojnSQr53gxZyILXNihZYYODYO7ZnQCFQQ+glaCbWbJY05ipHc9nZMkITDe0Yctg89x20CKmqSmmKRBQzlxoD0oSyknUBguQ8PFy2/wB298A62X5NMDvBuosxnONrjQoZLijbfeJjmL7bYM05Krq4ZVG9hcP2QcR4TYkwykzJgWNU8//SRX256ymBLbMz7/ypu89MQHuE6OVGfcmZwibY+Xjiwr6OUJdujphxWPh46x2FHIRBIjWQZ5LAHN4HesYsC2W2QSFEqS6UM6MWHnJJ1zNHGgkFOUGhj9JYn3eWgipIguDUopWpkYhp7gI22U7KJEW8UgHXmpyeN+d3gUnlpoIpF+aEluzUpMEe+hnAcpaboBnINJRAhNg8WlhrP2IdoLlvqQ0mQI0RDH7d6ybAxG5bgoKIxC4BlHhybiTUAKiU6CPAa0NHTRcuauCUEidU+ZFVSFoZQFREfnLUpY6mlO0JrESOcsNl5D6oleIXyOkYLRBpowYMxALUrG5BDJIVVNpgLN6ImpIncFqrd0vSFpRTTpPepYoG8MMtN7WpiLRLficHHKN87eRKSEFIlxcEBAiIBQknl5yu17z/D8E88Q80S7anjc7fdg6vkh8+0aZQxKTvb8cZEojObTn/gE/9P/8qucP7iPEC1KVzQo3r1+yPOnTzEPU/RkRug2TKRGZ4peJXx1ghu3jHFgJhRKSmRRkLmabbviqu3IZgekNCfaBzzoN0h1xdovuFivWdk3UEaS+SkeD9HukYL/ivO+KHKBABmImcbF/U2E1lAZDXFGJzpikiSlKes9w0SKDJtgdBHve5L1xHSGkoeUlDQysnYdYXNFa0+RuQE5ksSaSkJQe8h4BlgtyZLhSC2gzJFC4YLkwnfosSV5zzp0uLiH4Q+jRcmKXGeMXlDonjgJUFvqLJCpRBYs1nnS6FEqInVDwtIEz/WlY90/JE8aU03JVUEyAe9axKixo0YVAuVyIpGsyBjCBh+hzOs9DgKFVALbQ8zY0wtSxroLbLeCwgs2/QrnAh8++RhfefUxh4envP3uW4T4mAJBUVZMZzU/+Rf+Mh/++Ie5vHjAV7/5Jn0vadxeJjApa4Zuy3d//DO88rXfYd11dG2PyArW2x4ZWvrhAoPk1r07uK7jcHrA3cVNHr39mKPna2zq0UFTiIpieswyryC2NN2ODIkJjsF7NmPLbtiw9R30ChMSyglc32DDGY9XkmELMg6ohWadbdCjoQwZKbzvR4iCoBXNcE1ZTDFZjogBLWfENOCtQgvHQVEzKwqUCsToCW5klDlKlVjVIWLO1O812MRIHkcsYg+OpKbMMyRLhJ/jtCcz0z2awQiclWytey88rdn1jsZtSf2OmTSousThaEcPQVKaQKAhyxPLfMmWjvX4GNtdM6kXkEo8iiigiwW5z/bJnGakXbd0u0RXOvQ4kpcWpQM2BIQvKRB7sVS0hKDQukfIwIunH+bFk2d45/pdvvHwSwQEQszZ7UZikShNAj/gmsCgMnKZOLl5A3deM9ots4MnmK/WDOPIfHpCkU/48z/zk3zs+Rd57a1X+c3P/S5jMPTOcziZsm062nxDFkceXV8xPzzle773Ofpm4JWv/iG/83u/zcHkGCly6smUbDJjvb7P8fwJcIGjyQm74Yyu3+2xd0XNoamYFDNCyNgOI22/QyRJmXJy6ajqCh0sV+27LLMpN8o5US957Bq0X1P0Dms0ymlKJ5mJnGIM7//pCkIgpGE2KZgog4iKQZQUMUOpkVLsUDJQ5xVKwcyUhLSPHmulsdGh8ykyBkQSOBHxWmKiItaeXm2Z1QVTYQiiICS712Wj6KxFOjAAcv9B7cYtg+24Wl1Qxozl6ZPMipxxXJFGhRABXeZMqpIDmWF9pAobLI5Awx1zF5kJ3ACjSKSgGH1gNQyIcSDFNQUFUgjcOIDUaJNj9JJBaoTeuz7b5OhDD34kV4q7ZsZpPmejrqnIyScabw2rrme7vkTOBYaBcqqoY009W/DM5ON8/ssvk2xgs36banaCbFa04xU/8tnP8r3f8908+NYr/O4XvsTF5RXr0TOu36Bf3MP7lqtux5OnT6Fd4KWPvMjzz3+Ialby/Z/8U/zvn/sHfOnLf4RUiu2qh8t3maqc7NDRbne88tUHfOyzH6CZjQi7/3WOxUjL/r/SxjmuN5ccyglS5bgwoj3MI7gio9IlR6fP4pNjXF9TpIx13GB1T5FX1GnKzEzxdY7U7/M5uYsON1xTyRNyqViHkV56SgXzEDlaTCkKQzACrRLTSYURgi5kRCT9AJudxfoRLxWoPWHJloHcaUxuKJQAEXGtZ/QdQ1Q4RvK4d8qXWYUxiqgVZILGNSgtqPWS+fyIlEMeGnKlGPwerDlRUyZJ04vEpDxksiwZiZzOj9BGcrHd0IlmTyEYHVMhudQjCoESgsVyssfDySuK2YTTdEQMFRdhRdCBOoEIa7zMiKPg1YevcLE6x7qWm8WUSb1knTx+aJibam/emCuOmeKlZp4d85UvPObh48cMQ4uyGZvhDJEkt+/e4Cd/+ifRKXB44xZP3HiO1arl/sMvc7q4SW40i6On2bz2RZIUPPvSd/H8Bz/K0y98gFwHBIL//Pu+h7/+N/5jvv7aWyTbYkxYVOaMAAAgAElEQVTOOI5s+y1ZBttX1/y5f/0zYB8So8KHjl1zwXkrUNmErYvEVDAmuOh3XPeXtLZDZAW31RRZHLARicE1nMcLDvOCalFTxZy+CNhSkdIEXUxQf8J45X1R5MSEbVsG3RGGyCY0iCwxkZJSgMxzpnUJhUFoQZ5l6AjaJ3yMjCQkeyVKchapE23I8N6TyYyckj7Cduig6wlJcDH2jGNDKRQzLYlCUpoJeQAhcg6qE7IgmedTglZ09IzRMykyRB8IPtB1Hd619NajlCJKiXWWMWvJ5IJJsSAmiR+2RN0Q3JbMjPQykM0My+U9hBo4HxVG1UzzBSIKdjtY9S0q7Xviw+w2OxMYx5HrYcsiyzjMD8myCcFdce8kQ4obXMuEjlDGgiIYJIrRNoztBqEVURhu37rB+eUZ/9qP/RjPPvMMrfWsujfok2Rx41k+evI8vrlk8/h1Qhd4/sN/niduLtg1Pa+/8S10Bvdu36I6OuXkySf4W3/rP+Pf+4X/gNUmcrRYcv74EdtmICXBi8/c4MMvvMT1dsY//+pvUcmS5ANXvkP7ASsFVit2QbHqOvqVRcoC1YM6WSDyin5Y8XD9DnYYcVohhWFwgWu75bLtCRPHoa8J/n0+Xcmk4WZ2QhgC7bhGiIBUGUFHos4Ax8bvH8oKL2j7QAweFdgHD8YWFXsynZAxR+MQKtD7YQ+cbHta39D1noXJSVGy3u243F5TaM3tesay9ETbMowj1tQ4GcmLmkluCH5H3w9El2jtbm+EkALvOnqf8GHPPtz012yF5Xo85+bkLnk1J5cFXe4wrseKS8a+wbPXOjq5D31IGyDTrH3LScw5EROkH1mLlqx3BBzP33iWG/PbdLszpO+Q1mPHQJIgjEZKuFlMMFSMY0ceBc6PPPfkPT73u7+J7Tp0IRHikGeeOOWnfuov0XnBa6++xRe//IesLh+x2WxY7zbI0RLHlrX12Ms3sNsnQUjadU0KkRs3bnFvUSITvPjR7+Kv/eLP8z///X/I/fsPsDHRdSOjABkD5BPu7HJeuPdBvvLK1/brvNlAYTKKlNNGS+N2NMFxkB+ynMzpoqVPgYnomFNSlLd4zBkuego5YRYzun7Dlduyi5o6M/wJvM/3R5ErrRBFzTBsCEpiMo0CEAJV7HmH235k23VI59+75eso8gIwWN+Q24a6mqBkSYwjJg170ZULOO/wQ8TaSJ/lpDzRSwe5RqKxJtHqllxUJJ1IyWJjotQVmU6MtkFEizHQywk+jWzthtgK6jQjE5rkPbUscWPPzg+M/jUOxiOqfIqWOYaSuZpD7kmpQSiBax6DEBgxUA4bkgNvZsxVjcwWFOPelnGoCz5weI9byxu00xnb3btsmms2zY6tv+Q8tIiYc8vVzIxBBhBiRNdHbFNHcj1SS5544gnuPv0szzz1BNIUfOv1V3n1j79Fs91wcfaAwQX89oLd9gIXLBpBVR+wXp8zyxf02rJua7716tf4yHMn2M0VQU/47I99lr4JvPLK1/i1f/C/0YUR1Y3sukBWz3j9ouGjzzzF721/jy41qLlAihlF0FxvztlwRVbMeeLkGXI946p9SKvWKOkoxQydHZCHLf2wRhpFkZfcnS64RUUtF8zFEVqbf2V9vS+KPMbIRdqg5EimKqJR+DTifQeDpHWWth3wKFTYM/tSsEQlEBL6ccuwbRmGQCk9nRyocklVTrAaNBKVN6AN5BGlFbdvn9LsRvAj07JE5ZJKa5Sq6INHChA0XNucdtzhpCCJAmVOqWXkohlw7Y5pobDK4mVEBEcWE8MQubbXFCjmZrrHbETNMk/cNIess56r7RVn45ZMBqpshko1je2Z53OOFwfU7ZzTYkKnd9ikaC4fspYaxcjgIyEICJHkMg7VgkVm2NnIWbrCh4ZlccgzxSHZPYNWkpde+BjFbMlTd+/wiY+9xMX5Ne+89ibnVw958NYf0+0uWbUbghsJvsfFESE11q8ZuxafegqVMetyfLpBN25YHE4IbuTWyQ0Ws5pBwHMvPMurr7yOsYns4JBJqfnCu4+4K7eczm7yBw+/ipCCZUiM3ch6tyU/yHl2dsJsMcMoxSpJCldRe4OPlk4EjvObKDFhF9c0dkWUgcXyJhKJ9Rui+P/RGfSdOKMfeeP8VW5UN1GyxrmRFFqkcBTVyG4YaYfArXoO2pPZgNdQaImQHhtKorK8u7tEhi06L3lqcpO6nEPoGbQlaUM19uAyTD2nymrWxcDV6hwhDcIpdLCo5PB+JElJFySIQOcdPhMoubfHKTEnjBm7tqOO19jU0AeHip5h3dHbiCsNVo30xlGouHfP25xaKYrZ4X6V4OJtUiYpjcAOPUmPzA+n1GXONm6ZVDOmecmq29EOG7rtI0YXub+9Txd7EBmH8gY6K4hqoElntHHDVBVUsUL1ng/9qY/yoQ98hNXuik+/+BL3bt/g8M4p3kvqWUa1VrT2Ck9LVg3MbmpuP32bk2cmzIuK4ALDyrN6LLl6DCm3RNnwrXdfRmc9R6e3GTaWL33lKzB6tJbMFiV+Z3np49+FQDN0Z3zjNcXBrRxzJUk4GuuYZQVH8xMqHMHDYK9JWcEyK3EpwzhBheFwUpMJSbAz1mPJ1u3ooiAPS6Jccx0e4tL7POMZQ8JuIqGIRD3QDB1ZdICnKCImQfRbHo2WXEWqrGRaFsQs0A4tffDYShCNYWwdi/IIXSwIwtAOK1ISwJIs1ITYIoctQ0xoVZPLgov2gpqKyeSQQkUsHYPaovM5jBKLIk+gnKBNLWVeMZZrmtU5Vztw1rIeOk5UhR8VwxCQMYfcwDzHJ4Ww+92cLgWE75Fph6wbMp0TjKbIpxybJVkhsL5lZzukqTiSM8gkWQIRoG0uuBxXhCAxIpGZGjsOrOw5gx44NguW1Qmlz9k0HaZzfOwTL/LVV17l4cUZNkUebc747I/+KI8fvMFb919mdui5+z0nfPrPfg8vfPAZjpcHTPJjcjXDxYIYHLExXK/fZNW+xfZszdmDL/LO5RW6Cmx2gj/85h+RhCAROD0+Yad3fPrTn4IUOc4K/skffJEPHD7H4ckckfaSBI/EDitMdcBY1lw3O06KxJGquBBbRjtCPmeZ9uZbLy15lVMGS3SSfhyRMWCS4U/C2r4vilxJyVNHNylVtQftR49yni6O6CAwMrHx16iQU1NjlNm3FL3ChY5rtaOgoJ4fMmqP0IqkYOv3t2qkgM8Eye2Vh5WHrjknq6YYEdm1Hed+RZKGkypj61dsZY9CUpPjhcM1PeM2oOZPkwtLMorF8pQ8HUE7UIyGcdQoC5lzrNuOhylDqBXNgaDMSuZq/4C0i5Er33ExrDGlQEvN0+Y5JlXNOO643u1wQwRpke2a4CUuyxmkYkiCWs6YFIpJdYi1kW0/kNucFBWoKUfqlEldsHIb3vzGq/xb/+6/zYO/8V/wztkZX/3Kl7h56w4/9MOfxsqG2RM9P/MTn+LZ559jUZ1SiSVGzNCUiLSP26HWcBC4dfA0gSXxw1u8G3jnnQte/eLv8Id/1PDqa69wfHCTaZljUfzgD/4ATz/1BETFZz76En/77/x3TH74lFQUbBlZCk0Ia1KRMNOaRV5j3UgaHaYGOzZcxitK13F9vaHvBmalITMSrXNOgsIDDROcc4hvp8iFEHfZ25hP2T/D/lJK6b/+TqrHtdZMDuZoJNppHI5+aOmHAWkDVgVOsxlZVuxhoKZGhL0TZ5kfQZqglOEgO2TNjj44Otfho8fqhFEeMkcjA/m49/cMYkfXN8igKMoRJRMrc80mSHZiRwpwMkhC0tjU07orunHgON7GBstcVZSnS3JzE3t1zZgKrlctF9tLFnlGUed0oeXxxTsU3jGpDln5NWqyQB/NCaLEupIhrpjOjuiTJg0DIY1koSQJyaa/3qvHs+UeRe1GMpNzlCmOJhkzs+DduKbBkVV7vYqKEY+lsYltWyGCYjE/4dPf/0m+9t/8tyQveP3Vb3L+4AGzWyt+4id/lBuHzzLhFlL0KCyCMwQTouhQlAgiJIcQI4YScJTZlA8+XXM87fgff+2/5869J7l855rjZw6YTSf83F/+KTKtSUSOn77HZz/5Z5jkL3DefxMZKuK0oFIzZGyw7jUeD4J1t6KRx8gsEmSJMRUr0WIvr9muOgRwdHTI4fSARXFILBPddiB0A+Lb3F3xwH+UUvoDIcQU+LIQ4p8Af4XvkHrcR0/PiFEFUjWUkX20i5qd31GqjGo2xeSGIWWYbEKVCkQYiKaiknOEKphWMyhmqP6aPnR7i7Oaoo1nUh4jsg2jHhjGDmkUpTM47Tk9OkWkwOXYEjvNqTkgGpjInEwEttZzGRPFfIku4FauydU9xjxn9AmbJcqZpvSCpV9ytCzI5lN8CiS7Y9CJaB/Q+A3ONhw4TxjX1FogxAknxR10qqDvsTHhiQgBeZ5YHtwlr25yvdsQuvN9Ls0HHp61nOcjrbe8uV2R9I6YDyzEknyTY/yE+/cj0+S5fPg2H//kJ/nMm2/yd37573FyfMrl6oK/+HN/ltnkFgqB5D5JNCQUKTkiDogEEdFoEILAmpQCEo9gjhFzjo9qfv7nPstf/09+GW0C3kX+6s/+GywPjuiGDtXuqDdrvvdjH+C//NVX+cW/9kkeb8/Ztdd7ZnlwbNI5wVc4qTmXI8Hf56B4CuunrPtLrOlwZsQPhkI5lrlmqzrauOXSPWDjLnHp23jwfE9R+Oi91zshxMvsLcvfMfW4jYHrcYdSPUPYsBACOwuoITEKgUkalWZoJFWWkNowtI622xJzR67nKF3RBoEIEo1i5yNGKoq8oMhyqnJK10HyOyojEfIQJwecuiJTJWosyPpAYeac1EdkuSKXBhs90a84IrEophzWN5kh2a2ucLYhThRYT5UZ9EnGcpExmRwQTIYMA7t2L3h1dUKnGpNFtGkYMwGDJjNL4ujo/Zo0jIxAEiPTGkymscKTxw06bjCFxLkBO/a0g0eJyJndcNFcU2U53mnQPcKuyNYVm/WOa2f58m/8Bt/3F36cP/NDP8Rv/+7vM4yep1445aCeAldEMeITezY8HiUS0GNSQaTFkyHJSGQksSLgAYtIBwhZ8wPf/wJPPjnHxJJ//6/8DM8+dReZa3Zf/SrLh68jThbcni3542/+I9Z/9CLh6YRVA0WK9OEMIabU1S2kMKTRY7MdVrXsxJbgIqpSBOkwWiLmhtGUvJ0aJJZN7khHc5T6DmHihBBPAt8FfIHvgHr8X5yYAhtrGcKafOhR1RRdZQzZCNHhVb4X1I4ejSf5Nc0w0MdEliKmMCQRud5tMd7RdR2Xuy1q0lO+F/atUqTpz6m8RBZTCgkoiUkKETv8aNGmRpLhyZHOkNeGjMR8bhCDRHtH8h2XduTlr/8xbrXl8Imb3L11wrQueOw7Gjni6KldRO1axq6jmE5JckqtBE24Qg0eksGnjKkLSLdnOjaDRQTLWBvCYCikoB1fpRIlh9kNDqc36FhzNXhMmRNUwIlLJkdL5uKEVXOFE1BxSm8l7z4+Y344541XX+Gl/gdZHi/4xV/8q/zjf/aPeekjSyINUUQsDaDec/fsQZpSOJywiL1XAgDF3uggkgIxkrhEiYgsDZ/54e+m/OF7fOiJO2T5FOUjxSu/T+k9KdNwfI9CwK/8sz/kM8dHqHyKjFdoISk4pNbHWCTWXBOUZyse0e4u2TpPpjKqssJUNWayZAgBHTXGT5jGBX22RKgvfvtFLoSYAL8C/Icppa34l7yJ/1/U40KIXwB+AWA6L/B9x1RpjJySYkVuK5xsGbjkwJSI6IlJopImiwlTTajMEUgIWrMZW9LYA4LGbXi8O+fQLzhdzshCxzg+pBsvkdMMqUCqGqFyluI2Sgf6akuykZxECIHBGNpuh7SBZVlSVDdRJEYs1+uAKA65HBy8cckzh8csj2cYDGG3ou0tWgo6VVHkmkW1JJmM4C1yiFS6pDKC5VSTecf55oxrO2BMjqoUrYxEt4aYyPKC3oyctZeMyjLQMGQdNs1phzV9vEZlnphNkMOADLBbKZrdQN/2/Ok//QP83hd+m2gTeVlx9+4tfv4X/yLzeYYTDp8cAUFiBJGQ7F2pEhA4SPs7A/lekjWxx1XIBEaAYCCJnh/7sU/x8Os5WWlIKtL8/u+weOtt2lsnVNZz8MRd5gfwzTdf5sPrF5AHGaN5g0pbRgqM1KBzutQwDC2rdM2VD2g7R6bIvFyQUs04KmTYYTKJETWXzlOM76l3vp0iF0KY9wr8l1NKf/+9t78t9XhK6ZeAXwK4eesgnYaMZTbHKs+jsWGe1xRC46TZB5LTgNILrLMYCnS+b09JjqHp2HaeruswIsP7gXpZMaaW3NQc1FN81AjZoYoCZQT9YDF6RpAeExVLllTFXq2X6YDWik1r6WNHPsJMz8mLimE34MIWxIiZSAaneHt9RnlUs6wrFrkkunMG2TAoyWxyytHhjHlWE+2AtDNssmykxKcF7eaaZtoSCg1mhqKlTC0hbDCdJM9KdJZ4pK857y9QyTGmgiKbcj12bOwK7AoRHiGK2zydbnP1eM8J3/Yds9kR1fwmt2/e4mqzRXFJOS2waqBnxNORCAgCJLFfqSAgyBEpoIUm4MgJRDyQ8S90VQGPTPtV6Ru3F1y9pohS0W132N/4p8zaK956912e/0svUT/3DB/96Et84YuvMcR3EWNJk66Zhhk6eTqdEMHS9pZV1yGUI4tHHHAIYt+eeZuIWcOV2yHMyN3iCRrvuPaP/8T6/X8zXRHA3wZeTin9V//Sn75j6vFIZC02BLej6z1aTfYfpRNoMePKWpKyVFLghoCVA/Nijg4ZO9exsQObbkBZh8xm5HXGk9OKJu233EwSFGZkFAtmxYxSax74BucsF9stJkoOizl5UQMZ3jvGboO1DVVWUWSSPBPIYEnjFtmPKNkxyUZkqbjOLnh7o2jtCaNSZLmmc484PfwgR/kRGkcmPEruQZ9X7Q6nA1djw1Xf7hmQeUGmSlp3yUSvaWJHbzLqqmFwEek0ejB044SNWpNHxWWzQwMTM2UcDrk9f5FqPeH15i3wEIRgu21YzI4otSHXOV4ZplmHZWSkwzESY0SmYW9xk3GvABcDMkGMnoTHJ49WFSl1KPakrZD8PmcbK2LWo7VBJMmXf+1Xeed3vsBPvPgMy9FyvVtzXE146aUP8/v/6HPcdfdokFy5gpVTkFoOSk8lNYSCiVB4YVAMzIqMs97zePOAvBqYpmOaxy1tPOf2EwfMqxlZvkTJb69d+T7gZ4GvCSG+8t57/ynfQfV4JHC2ecxjK1j4GQeLGbbtGe2AFoGVteR6wsE8IlJCSfaQHZFwNJQycjATLMQBR4vnOA9XPGzfZJJVIDIebK7IfWSsFLk2TKoDMjElhJFudERniQSKYFEyw8WBHpgqSa0FM58ou54oJZWB/ijneHLM6a3lHvyfW3R9xDomdEoUaAr5LCf1HWaqZOivMdKRpOaqb7h//S4uNzxorkBMqesFbtdhwwV9GNB6yVkjmdQF13FGGjsmbklPRJmBKjeIVLD0t4gYjmPgYUqcPTzj4uGKaB0PzlaYQhDDiO87hscrTCbJsgyhRgYxMMaBGBwOj44CfE+AvXhMZxAiRgliCGgSggYlNFpFhEhElciSJykoRYY00H/tZT7wlT/ifBj53Bvv8tMfvM2jz/8WBz/77/Dic3dRUZCuA0PV0cqK3N5EFUsIhvur+4w+YPI547hhSsUsk/SiZpRLClETpSVkmtQvOGtaJlPNUnybl0Eppd/i/yJA/t/Pd0Q97m0kbQ0qalQ5oQBWmyssAp1pQvLkrsd2EikTo7bkLpBlNdOsJpgeFQVVStRyx6nQqMlT1Isb7NoN3eqMq3ZDaSYM7YbrVNElzS46JghCcAxDRCbHUjoabykTHM5nSAsrt6Z1AZXl1JMJ08MTtumYgzzj/nbDw9V9PCPIHIlEioJJBXbseOiuGMYNq2SY6hmX3tMg6MUGNVFkcs5pkVO7QNPu8OIxnZfkMafdCtzQYdKGtCxJyiDSnOP8FjM5oZu1rHdXWHdNXif8bmBzdYqWmuurM55+5iku1w2n907JJznhzftIOoJ19CEwjhZnR5IccdGQ/F4upghEYfdrwhL26dFITBqVAioT5Jlm9AEvJcr2IGfsvvUG5a//CkeV5JNP3eLXv/UuXz+d8qm/+TcR+YTD4zllanh0fcWbcaQ6PeFmfYvl4iY+XvLG8DZGLsDnxG7Ced+TH27ZJgd5DynH6JEnbx/Q2ilGKW6UBavdluDf57srmcyoZzlFXaFcZJFLhDc8DisyUXJU5YigGJoRMTFMoyLYyOPUcjg9oPAZUifykJMGiw0byDNi7NiMO3axIXMesQ34KtGxoyjniLLEDxf0YoCUUVKzmJ0yzQpWm0c8vHyAKScgDFUckMlDFxD9SE9CL5YUUnEwWfBoeJ1ZyjHV8wjtsamlHxqGrmeIidZ5HplvIeQUX1ZciWtiH7lZKbxLPB4tWhZkaY4LO5xqiDFnCAqqjKysSDLRdoHttifIjjZccmlXZFIhtESXFUIIzi5XXK/WfNes5Prqig9+6EnUnROKr32Fo8FzXQG2Z7ducKFHEvZTFS+R0u9B+VKByHCjRSEQhH321QWSFvQmkqEZVEKmgtDtGP/u/8Bh0ujJhOfyIz6VTSg/++NMPvFxVBQIpTm5eYsuH0jjlNw5Rv8Of3zxAG8aLHtVobMDqY/0yRGkoi5m5IPZp6SSwEuBDhGTenYkroYO937nk5e5pp7PiEow9oGBjOm0pLUBoqNIGU4IKAyH1YyiyHEClBds+sRG7L8Qbx0ze0XnNmz8QLu6/15SaM7BBGai2Jvfui2okYPyCFkes0WTL5/hIx/9ESb1lIjgfPWYr7/6Oa7OX6bOF+TzBV1w7NotITkUkovkmBQlQeS4AUR2gGYgjY5+HCnzKcv50+isomkHHmy+zq65QivNXX2HzliMjVwxkmca7zwuTJgVR2ziIxa5p5pVjBKyXJN5aOPbjM5DrNk0EmsWSFWS53tFZO8D55dX9GNLTJrV+hJTaLTSuA++QPmbv0n2mqb/EAzNyG7coNReVuBHRwKmZcmAJdKh435kGFKHihVFoQhth84MjVOUSiGkpnnlbfKLlnAyByGwRzf40L/543zgR34YU04QwLSaM/nUHdIH3mA2TJiUgSJ/yM4LMJqJXFLqGZnyXM4HzLAm45qx65D9hKOqIpgBgaBQcx6Ob3G1ehnvJ+j3ezIoEJjMNVIbyiJntAVSGBblfkMxOsVBPkXXhuNySi8d3raEEBlby8ZMkCkncz2sBmz0dD5xn7c5On6G+eQ2NmvRwZD6LQMDahwZjWYxPWSuSyY3nme6PKH3ifP1IxKep5/4KJaOze4xxipEkhT5kst2RfSB4CUb11OaKTN9SrKGlbtCu8hkcsDB4ZOoLGfwA2kUZHFGUVwzUwprFGU/oQyOzbjienCM3mJ3LX55jPAFRloOihKXJJfbFcv8BrePvpth94jt+jGHhyVH05puZxAhQxtBHB7QDA14WJ1vqOoC6/ay13DjDsPJMeLz/xT1kQ/R1HuEtHYNbT/ifQIUfd8ggqOuaogeLQ2IxNCsMTLhuohJp+y2VyyObpHhMf/w65Q4knOMmUH9uZ/mzqc+QV6WiP2skel8xuJ0R5HnaKALLVmsuVHdoVEBrRLeSYRxLIzi+NZNbL9iffWQSrUYc0qjL5nkFZPiBW6Fp9j2a+x2Aup9HmS2YcSKHTfy2xSZoN0pMqGYqkPa5oJeGQY9ksmRQZTUxZSYJy63r3M9jLjxNtZtMb6hj5Y6lsyZU7Qtk5OaiZ5BiESluLYdKTpUKrlu1jQeSjWjvf4W26/3bDc7rtf3ado1xSzj/6DuTZ4tyfI7r885Pk93fPe+OV7MkZGRlVWVlaWsUWqpJTWtBcgMDNTWizbrFWzY8xewwFg2LFiAgRk0bY1JmAwkWt1SC1WpurKGzKwcI2N4EfHmO9/r83QOi1fAhmSJBWfvZr74mLuf47/v97PKTqGpiYuYMm+wOw5xsWTL79DvHl4HlUWDHzgUucGqsuiYLUa54smrx6x1jVEvWSxzAssi6LnUKDb5CtFUYA8whYudCUxcikwzaWfsdruYdg/deNSlpt7YiP6ALXvISrWsjRptg6tMStPAMwO2/RGn0fx6ahBN0WR0pcNmE1NU1y214uvfR3/2EepPfwX/9oAsW2OYNXVeoasWLTSV0piGoG6AuqUuJZb2oAqwLIlrORgORMEYz+iy/mc/ofvzExzLJVUG5u//A/xHD3HciLpV2NfN0nQGIbf23+JifUapTzCEpJ37iNBg7JmcZlcsqzmRd61BT9QORbNmU5yx7nYIWov5fMIgMih7DVpaID0cQ6LFax5/KxtNmbcsnSUDwyKMHMpWYAiHttWE2iWxKmabE8pc85Y9oLUipNnFMhdIo8QrawqdkxsG2rI46u5y1EZ4QcQgtFjKgrRQpKqklQ1228e2PQplYxgaUW5YnvyCNM8ITMVBaJJnF+RqzbI1MHBRTYVVShwnIKtSklWM2XFZs2CothEOtNplURzzYn6GblyM7W3MtiUKQoadMaKtKRQIo2EVP6do5nhWgBHaUBfs3TUxrSGmkyFzTZ260CpGTg9fmUxmz8jrDK0ssjxnqlI6/V0yKqbLcyyp0EJiGBaXkzMO+l0+//hz4rRFiZZWKKrf/gPKf/7fEPxkxum9Gl1mQIuhDWhzlJCU6XVAxbBDIrOD6WlMr8Q2c1p9zmaVYBtd0sUK8/0vUEXLOgzh7/0h3a+9gWE5CKmvv6FLjSmuw9vDGz2mryasjnPytuJh9zae1yVpJuSYICLOyzXupqUtE6JwiBsq7EbgGkMqKbGNJV4VkyuXvHWR9gCJ85V8vRaQW9ICaZGXFbVpEIaCrKgRwsQOIyJhIZVkupYF8isAACAASURBVIDJ5ZLT4gV4JoZt0YgCZZzRiyKc3GTRSCrDJOwPiKJtFqsJ2XpKka4IjICO7GF6JpXSJE1LZHcp2glpmWMbIaPOPk0yocEm9CNGwRGnecU0XZG1ktDsUIcNZaaY1xVebpGaCclqTqEaWt/BKgrKRmK6Jn2ngygVvV6XLadD1WgaLeiWDUsRk4sUx+vi2YKz1ceMLQfTKUj0JUiJrRIiY4vcLBh4NrI/ZnLyEco06fohZaHQKkbLiFTENE15PX8vWq6m54iHX+P09CUXV5d0BwNsxyEcjIj/3r+L/7d/TqdIOe4WtHaNbwiyGuzcQGuXIDKJ7AKsFZs2R2c1ZtOgpI8sNU7bEPzVp1iXG66CAPX132Tn7j1M69p7X1UNGgshNIaSaCG5f/g2z88+QVgWD7u3ubk14qpNqGTD9pZHLh3cCTx7OaO3e4/7o0cY1Y+I2xe0quZbN98lkX9DslmQVwWt1FiFwf/LD8/XA3LTkgy9AaaAqjG5WuQ4lkeirjdFSloU5DiBTUiHV+uEeLOh1+tQ2AbdUGM6gsQy2FcRVWGRlmsCL2DLFiwWKfUiwx/32B4MSGTLYvmS6XpBJjd4RsqmiTkcvsOeOWTVxLS1JjKGoHy2nB4qUcxFyrmOMaoSx0oQgUFcp3SFonU7WKql1QJbCPb3Ajw7xMRHug6qWvI4m3Jr+DXEcsVkdgJGjS9akvw5y9ShKLvU2mc5X1EJF1nZdNyQypog2prSHjEObuD0B1CtsSKLrqEoqivSStN1Awxp0gqNEJq8KpgtppxdzvnJz3/Jv/MHfx8pNaZhE27vUv/wD5j/J/8h1vf2KA4k5yLHbAO6fXCdlKzckBYKQ0rqusQyTCwsbKvGzQ36f31K84sr8p0bXNx+wMHbb+P7HtKUaKERhiTLM1wjQLcSaQm6/oiihb3dLkf9PTrS54vpKU3RkOdzAlcg7YSt3Zg5L3m62WWWz5nFL9j1bmBpxePTK85eNVTVgq2jCoM1Sr3mR4itUpAqnM6AgdcnbzZYokNepkhD08oWVaa4TkPourj9gFoabNoUR0X4IsI3I6gTdp2INjAo2imUBb7T4cpp8f3rov/AC7FkQ12G9HVKo5+SxhFJK3Byl8TO2bI8liJFaEmRrumGA3THY5PbTFYn7A/HaC9kXr2iaFJkGyKNLlkgcRMH06hIVQ65wLJsQlsyS9fM6xhvc8b59IqmKsgsTdFWCF2Qy4Lt8JCe4aM2JbQ9VmJN6DfkOsXQAcezZzSiAiuhMVpMqbFdTccbIdwO5dLiRVqgNGilaSrByfExhZC8ePIcxxbUrcBwPfyygO0DRn/4j/n0n/4TVgOf/d8ZsfYa1vWKqrVQdYFGowV42qJqG8pS4Z7GdD9OKScFk/6Y9r33GN99g34/wvEsVNsgpUGrFFVdY9YNWttofZ2vtZyMnrHDXGw421yRFoJaCdaLJbWt6fRswo6F0guu0l+wSWMCs0Pbrvjp8UccvyjY3fexXAtaC88y0OL8K/l6LSCXQFkW1HGNa1e0hokjWqRdYxs+2nJI2wyjVtTGmiB0OHLuUJYXeI3A9HdZr05QyZzEFjRBi2k6DKIBEpMoGlI2NYXRcpq8olIZqZrSGmuayqZj+Qxtj4Ed4RqChAKlWzbJgkm+4LaUDFzJSzdFGoKbzj5Xbspi00EtGwpf0pgKkbm4XA9RXZUlodK0xgVBFZFUCStrRlaktDIk6LoMdJdZmqKNhgf9LW719lk3EmqDNnbouwa5LMnp4DcWnuNiqBqvNEkNG9V6mHVFLVtsZVJWKVWS01YlWiikkCzymDDoI4oUyzEQlaAxTWwvpEVx8L0f8nJ6xeTDX+B9KnHlmmmoKPzyesNfV9RKUuYlYlKytyixlhUrI+BstI/7xtu8dfcRbuBjuzZIE8N2sC0XKU2EKCnLkqq2cQwDVSvOZzGBDmj0hrR+gWHfIpBjzPBNTLVkoK+7yedijqFyKp0QeQrhxlQTwToR3Ikkbx/e4fHzitPjJajX/AjRkBDZDt0oQqqKoo7JtUFcZhgWbEUedVGyTNd0LZOqnuH7Bo526TsBsRRMyyt6FGRWCk1LXWmugoSu4XPU2yGWDkM/wtVrXs6eEBc5WiqG/oibg0eYeUnXLGjKnEk1R1cGWV1hebu0reBivSRetgztXa7WCXmWYaeaVaMIpc+ut0uRJ+y4IRs5YJ4+ZxYvcQtB6O1h6AAvbRkYPVamIKnm1M4G4Zp0TI+DcIu7oz0WVcPM8Oh1e+Rph0/XT+lYPUpsZuk5nlHRcw3MPCErV2wXK57nU65EySi/ycUqIS/y6+A0JnFV0Q0140GXotTYQl43wAqFFILQDbjz7jepXYvTxQL7/Dn2B0/o6IYPejssqpq8KCmrBt/y0bnBsqgw795G7O3z3hv3kI6J59jYlo3reqhWoVpNIzVV02AhyIvyeh69BlmOOCuWOEqwLHzcgWDoSrQlCa0I7fjsRwGyjanDGp0N6PhQkLN/6DOW20gxYHHisnqSMbB30c2vvpKv1wJytGCjYkI1RZUS3/QIgy1YL1mWKxbLkiReslrFbApB2K/phx5Rp48dwKJ+RuAtaFyLSzWj07q4jCkXOcotmNkuVVsz0F2qqiatFbL22ZFDtowBhhLMqxWzyRXrbENjtPTsEabn0LElRZnQWDaj7hjXDBhIwaKG2t3QdSQ+kq5pEToelvSIaPGDmkSGdIs+TlVjeluMXIe+PcBNplz6BVqtyYw1gTFGlzt8PH/Gpj2lqn0iowu2RceO6HtdQueA9SaiJzYor8AwFYEwqa0jfOEhlzlltYW25782UjfUQqEbiNyIjz97zHy6pt/rkSYxqioxHYm2JUFnwGi8jWV6LN2Qf3MyRS7W9Lavu9R1scEyNBGK537AJCv44XjIg4MjPM+/roQ2wXDsX9sooG5bDMOgVS1NUWLaNgaSUikc3+doMETrNe0ywzE0WqRs9EvMzMNpHDrBPnXscOUbjHr3iDdP2RRThFHSG0jCsMdilRLudXn78D3+xf/yr74Sr9cDcgHrak2w0PQ7A/ygj+v1SLMcWSuKKsUTPq1uSauG+csVxSjH6+yxKsc0tBR2RiltinwLhw6BHdKaEkO6rMqCJF2RlIJlNWMVr4mUhRYa01MYlbo2Q+gIw7JpzAQiiS5LLlbnNFXD3v43CYMeZWkQSJNMZzjC4lH/B9SrgqvVOWrdEjprChsOogds+wYbp8KvK8AirRIabHpK0LZbrJUibCN2wl3qVrNMz/ly/SVGI/HdiE49pmxypE5YFJ/TMQNCr8OmXVMLi3u77yCQrE/+hh2rS12vGR8EvDy+xJQmpiFRqsayJB/+8n0uLi7xvYAsj1F1jaVsDKnxDYOt0Tai1hiG4PabX+NP/+LP+b3pjAd1jqEUbdtywwv5H5KEcH+fneE23V4XIQ1sz8WxHQwlrp/i4vrrQZcV0rAo65ymrUHYbIVbuCpH6wuEabAVKDpRSJInxHlN6A1R7Zwni5x5nBHZ20Suw6eTGboc8XDvAb3uLrcPHrHIzrnondCa58jXvnelrDg+zdAdMNwAo4yRShLUPsLYYm5sUNrC9yIOIpO2srnyKlJLUVdrHrj7rHJF1SiU3aUnIyLHJwxc4mTDVTxHtQW3oy2uyjVFFdNjh7TZcOVJ6lVBVlbsegdUZsMqXzOPMwIhiJNzzK2attZYKmTdNlyUJmthIBwLp3RQRs6inVFMErphl7Uv+e742xQipVmfoGooyikn6wsCA+SgS1VZ+MLGtUbINKFxW3p1j/3ykKRKqeqM0/KEXNn4hsd5+QVjv8Os9PCqPhdFSeb8GKkKan/CN3Z/n2m2Ib79hOhnAbrKyXRNq1ou1nOa1uXx48+5fbBHXde0TYWQCt9yiDqda3dRXgGCm0d3+K1v/Sbhiw955FtYwkTVLZVZ8H3bZfPtd9g93EMrsBwb17WRjsN1Y7ZC1IqmKHE9D6kdLDnEMD1s26NRNRU5q82Cg+gNkmZB1+pjly2J3Ma09kjUjKxtyf0GXxcs1h+xFT4kGm4jLYtynfKZ9T6yMon6fbL4QzBe89kVtIlaKhYadocFus6ZVRqnBNqasi1ofZM2aGhsn46xhe/ZhH6HWZ6zGxywzjLm1SVWmyBkg2w37PgPmOQzpuUJfXqcp5f86uoJlrbYDiGwdsnjllfpkrPjC26MNnhjg/P6krDqgxMxbRNudAXz4gSLI5Sz4byu6MhHBGbD0+WHmGmNqCyEZzFNG67iDdr5GD90uUhf4a40dVUQa5vY0vhI2jakb7pE4S7T8lNCJIZ2kNKhySrmYoPpFpS1x8W5QWkGXGQtTuwg9AWpTllmNdvjI4L2Hi+XM5CScecQ137GPKkpFbS6JEnWBLbgFz//Kb/72z+4BhF97Sr1I+zAJ9QwKLuYpkFbtzz42iPON2d88vIFoKlqgdSKuh8wuTrDthuGwzGLxQVN3ccwLKyuc22+kAJhSIQWeI5DraBqFEmc89FHnxDPV2jHJtPQyg22tJlqgZIlhsjYGBM6SlC2E+bJksA+YKff4XJ1wXqRMMuW3DC2ibxdeipEyQj1uveumKbBzYOH+FnMxWmFPBS4YYUx2ILGZnp8TEd0MHwbB5+kqTg09ymzlDzLed6eEqsVlddytd5g5lfsBS6WblnGKZuqou+abIoSmWhQFamdUPmKdXrFYt6gHRPd87C8kMNOn6E75DJbc8v6XQJLcXrxCw79PQqzjy2nGPmcSXbOVTEjKrfxpM/VMiYpJVVeUuoJD+7foFlLvpyccLR1QM/pUNk+RtViKw9dtqi2ZrIWXJhnPAjvYHsmVreDtTxgMf+cSuVUUuDYPYadgG6pyCqLq7REZgHaUDRak5oOO4f3MSdzbMum0AqtW4SArM3pGH0uTl4wmyfkeYFpgWHauMJGWQ4iUIxGI2zHJ17O2LK2KPpjLp48IW/qa5+RUJBJGlVTNy1aK5IkpUgybMPGMExcy8bQ8tfaSkEN1FVNIwQXx+f81//Ff4V9Cx59/Q0aKamrQzZNRmdos7fzNjUz1stTqlVAfGKztBZ0By26abH0FcIuGLk2A11R1i9ZxxVdOcASwVfz9f8dyl+9GjTDcYha1lzmDoFu6bWaKl0y6m9ztHOHZTnDE5LGECRtzfnqCtVs0GheFFN8X1O3itAoabwpLxYtTZrhWTZFkXAlpoyCHk4gKVSDsAWVEbPcuqCjO9zwdhn4IVgNL9JztNdSuAmOazGvSvJccVFNcC0HYfVYrSfEZcLDzi2KsYHEoj/4Lqv1hjytcM0ublPzwN7D9HOOOl0WhYGsHVb1nLiu6EcO8eYK27WpmphVFpNWNVL3CVwb016R2kvqzCbbNHw2eYGtBWEYMu6OEGFKbZ4zb2JCZbGvKuZ5QqU0rdC0okEiUDU4QjG5eMUqXmOZCqElNJA3FY5lY+oK07aprBqVZ1SVZufdb7P6+Of4iUbIFrTBtG3JVnPCKGRnZ4xUBnkes1jOcIMAx/FwvAq31ZiugZYGtWowhODjzz/mk48/5Lcfvo0UAefpc5KmYTB6jLDOqMMtzNqGdcTTD9bMrxT+nkdZlWTDCwZRh9IMKZNjzueC8eENlvELzrJrAdpXrdcCcsewebU5pdP1uLXbpTRKtGUhTIus2mD510lxx/DouhFFURJYY1bVinn7ko4cErlvQLpiUTylZxxQRB6+PaQRS3K1oVIxcTGDXg1sSMQWWoyo0xrDrrGETVGbLAuXaRWh4pKSmqR9hmdW7IygTiaU9YjAustV/hzDsum6h9wZbpGLKXVdMOzsYlBje1ssVudEbczXD3eQkcLPbNY5VLkiLWIG4xGeW5CKpxQyZZF7OE5D3FzhRQFZYlBOI3KtWE8uSOc1pmeRxC2d3MY3HcywT79nM21P+eMf/xTm/WtZlG5/fYzYYpviOuw2n7PczBn0urgaSqmw2xJDCZpaoduWTTyn0jXxOsYfDjC6I2T2koaWTAnaTHD6ckaSNWxv7TLeiwjNAbbpES9XOLaDHwYksYFuFZbtoBTYrsFmNSON18yWG5aLGr9qkWbFYvOMlpp23tBPDmmn+xw6Y771VogzhNM0Q/gupuuypUf8/OySSdJQehqUy45waV53nYrUirKt6PYOEDmklUIbDVXynLTQPOzv84Z3m5fphHkVk+Y1L9IP6QwhVSmOMSRX0PV6lOtd9g/epBd1qOM163nD18NHlK5mWn1Cqwz2rX0Mo0PdSAaRze2RzzqTbDYTFhkY3jEvr9bI6h62YdD2A7YGmrWzwZYCv4RH228T9UI6rcmBdsh1Fy/yqeyA08UZjZjiBh6irOlaA2rdorUm7PSpVcpI5ig5Jw4q6qQA2aWVFbUSJPYFTiTouj6GGVKJE4JQkBmHWAOH3v71NUWyyyUZbgabJKNKK3Z2NNu/fY/pn05RaQ0IRAu+NJkmOS8ef4n75l3MqIcrBbgtcdtiVg2WIQlchyqrWVUlXaERW30mr57Tx6KhRquSKi9INxt++sHP+b3o7xCNxvhBgGEIiiRldTnB3zURfohpmrQIqlYz6I+p6pJX55f0uiGDwze4tzXk46uS+WpG3dZgdLg7uk3hC3x3wbI6o7zMWU+XTNWSdGVTLBvGO32OXzynbm3yrS0a/ZpDXlCBUSBrxbK8oMpr2jbE9HLyOuXlzCSzSqQSrJsJNgHIlDat2WrGSB/CakNWZ5itRqU1l+WccnPBIrskdHvMrjasHY/V6hNmesJv3XsXf9Al8xN62+eMucHZiSR95WJrD6IXtAgMbXPn5jfoipxF8hmR6ePVa3aDEYGwudocsxIHPBg+wrYrfrT5iFwlyMKiqUakucvtaIyyO5wsv+RZfoVjSDLjEq22KGYGRTnGkh6N6TKLN7SqpB/u0Rn0sIwYx3zIsvOc2rFRucd2x6Pq1Jydrwh7Xbphl8tLzdZ+l0EwwvqNgo3e58f/0ylt25C3ijhLULXmf/7n/yPyj/6IozuHdMMQz7HRukGplrxusDyfm0c3iYsvcLSg9gM8LLTZ0mlsSq1AKqQSLBdLPvnsc77/vYi8dPFcD88PCXs9yqahbGpsaRBGPjovWE7O0YbGjba4u/911OxTvpidMJ2u2CQZojKYOEu2nCWy26MIz4lXK56+3PB8dsXWA5f2osuw9Ok+sImGc4bhEZT2/w+MzI2Ja4xJioKiVDiuQDQWfhOxLHJqaj4vzzjqb4M0ECRsRzYUEl0ajHrbRNLisihp8wI9uyQzG/L2gtPmmGFzSJyU9Nx7BB0LaTUc5wVdSnz2uTgPsXwDgyHKuiSId8hTl06/y1bochgOuVpdYpUWdTZDu0OMxma1bgldi50wYpoXFJsznHbGps1YrDo0VomyBPs7R3x6/oQPF6+owit2dx7Q7d1AyzFtE3CVXyGqhMJoKaYZRZsQRxm66+BbDVvBA3Ai3NCgWSyYXsTYw3uMDg06wKoquLm/S9iR5O2KRl9weCti5EScpVOUhk1bI6XNsyef8q/+8q/4D3b+iMBqyKsK0zBp6hJDNSRxzGh7h9tpRqUahOVQSwWNplUthagICk3qWtily6vTMx5sUoKwjxuEBFEXWTUkqzXSkvQHWzimhlDy6ZePyRqNrhs6OzZFuGCZlqSXCXUJ27sD2sWGX33yMRthMLidMV/PSfczvvfWu7im4DR5hb8bEwYWhvS4ORqyyS4R5mt+hFiUDZtNQsfSPHv1lGHQo9EzOqGDi8HdrSEz5dDqjMXVhC2vxzBwGHX3yKKQaLhPGU+I7B3iboVp9+nLBKt22K62kPOcXW+LsDOizhuezM8ZdSTSKDj5bMqz6hn7uwOO+gGRren0RnScHq7f4cr9Je8/fYlndAiETbcz4mD3CB1PaGsDszCJ10uiSGALm6LZZi+0WDYvyJoVTaEptGYSZ4RmCOT0PIFwbpHqFouGaJpzVVwixyPGB3u0jr7uqsoatCXZGeyQVRklJ+x1dzjYukuLSypq+q7NxeqU/TAkTjIcZ8zujd/i1eSEjfpv0Si0ljS0uC5UG8nJl8ecnUzoR32qusWS8jrsrgRVVWEYBuOtHqs4ZrWJWTY1thbX6kjAlxDnNappWDU5x0+fMuyP6Gkoihw7CMD2Edoi9B1c10RLyTvfeZs/+5M/Zjp5wmI9J1K77HUNet8asbszwgsfczlpKJYWX/yyYPaixAldepGDM7jBgxu7dLcj0vYTOsUWx8tXPN3+S+wgQrzu5+SWDWmTY9VHmMkeB90dShIc2WO2inle5vQ7JpbZx20zwgToe0T926SLmMlsxn6oCYRFZjiYhqBvDXlVAnFCTMOD8RaFK+g4DnfkTQYiYN+2WIlf0hQJKrtF5VXcjgbMZlNcvSZPchxchFlxPxrg9Xzi2mard8CxOcSYvaJsJHFasNg8x0Fg33kLv67YKwuepZI7/YCiWXM4GCDKik4UkbYRAoPdfp8oHPPZySk9a5d3brzHpFqirS5pnTJPcrbrPp7hMZI9pvkUa7yPwOR8+SWtnSFjyaFz/aPlPD9DlAtevWr51a/OqYoaMEAoyrZlGJnEcctqfsaP//LHHO7u4HsuHdtBmCZVWWBqaAFhOxgqIS0SOlpiy+s5+B4WQ8vmeZYALUrYPP/Zz7h3/y5h4CEB17LpjfcIoxDDMpCGRknBP/xH/4C//dFf8/Nf/huK1udiNqcTGjjde9hyRBY/I3Ak/dGY5muXvDou6YxaNngsr16x+/ZdlAuLWKGrhLf2ehS2z8WyBfWaKw5tYaKmkmpo8o1Hv8FuN+AqvWQtXPKlokxzPGnS9Q3evfMIo01QHRvTaNgkV+z2trjRH5MlKXWlCfsDkrKmja9AOdhGj7aE2JwxN8/ZHR6yY29TNSW2O+Yb0Q59x8ZXDkMVcWKc0wn28IwVYzlmU2nSSoBbMup3iaRGtjMAhFxzOV9zFi9whUdVFRx0urzVu4UZXDCQIVWW4HgW33jjIRtzw8CvKTYZk+VzRNvyxhvfJXA7aNujPhXMq4LI8Aj9LYI8I3QsPlxvmLQFTXGG1yR4fkQlBFfrj7EHPUJnhBuOOD/7kvhqwsmvFJVuAAVo2kYzGA+ZzM9om5bjp18wn20Y7o4plEZUNVKBUi2iAdOykBa0pqC2JH3pUuqctG7ZqAZTNITaJtcNp1eXzL98Qr/Tp1UgpIU3aOk5JrZrgNRIKQiDgP/0P//P+I/+8T/i5dMVWbKhOUq4u9NyUfyELPegHNImJYVa09lvWVJwFO5wYT/hR+fPmF7MKbVix73L0m5448ZDxsOIf82XX8nXawG57/p8++gOOCYHY59KnNJaDb4KeOvumLu9IX1nxNnZY+50LdaFw3k1oXYKevs3eWq0qEax2wpcNyAplrycXrEuFbmW7GwNaa0rdG2xqjPuhwH9Tp98MeOdnbeoBBTlnMhzmCcn2FKxt3UfVU04m57QdQ7wpUMqVjzPnvDk6gNmOmTUuphNxabJ6DsWi2ZOoo4RyQ12JGyPdoiTFiNS9JuWLLsg1QliXhInCV5ooacvKOx96mJyXSavQo6cIyoWuEFEKitUtiAvH4MyKfIVFTGb9RmW5ZBaGR29yyqL6aqQvvcWejJnevUECSg0WkvaFvxuSdizyGY1yfqCvK7wHJ8iTqibBpklSG0ghMQATNNCCYOXlcaQKUrVVFz7T4fCIdEVLhYuArV4ieB7WFKSFRmz5YI3Ht5EGFwXJwIIwfb2mH//j/4h/+TP/0uGgwHLVcyTF8eYoWTb3efBTQNyxQdnNeVcEHZ6rKpzWm1ST7ssJwO2+0Miv0cun/LF+TEXk+t7/qr1WkCe1TnewRb9gUsgBPNkiWPaxO0rVn7O+8Uv2S8eseX6PCsrFsmCXz19jLIzDh/+PtodQl5wNntFGIS4XY/C1Gjnuk6struYesBZMmE/uMuReYMWm9q0mRZTzNbDdTskxZSyNtnzt1llJ8zjx+SFT2/kMcuXGAw4m0wx4pxBx0W6JvM0p2eFzJM5ph/yzd4BfXeL8yTGPk05qa94W91j0SYs5+c0Xkg5A8eyGPXHXCWXVOqSda0R8Yp73R2ysiAya1bxKbFhIXWEY3VJlksGN8Fwb7FXedSF5GLh02Ye24chqrrJ127/Dj8+/lPWy/dRWl2b/4RG64bT8znjewbHc6iblJOTV3zzG99A1QLHNJmv1zRpzs6oTxhFeEVL5NucGLBsWywhSZG4UtKnolaavmmQUjN422JTTtjb+ya9wQ4HN44IAxctNQKBEAIUGGi+9e43ufm8YDAo2ZQuq3UFyqUf+sxmXbwoYX98m8TZpttdcb6ZU1cZVrThzu0bhE5A0v4E37FBhQzCnEYnX8nXawG51opPps/5DWOI40jGKsLUEU/zE06yC25tfw0tLGLHw1EGtXYY779FJ42Zv3yGnz/lSrpsJOz4kjAvebr8ktxYUTkbBv5bVKnFYbjHneE+66rhcv4Y3/U4W75ib3ybUgmUkdLdPeDLy79lmZ5ze/QO+917rFVF2RZk7Qn1skTkDr9YnPFw94qq6DBdm5xNN0hP8K2dt1g1JY2zhzIVP/vFB1RpTCdSePkOTDImq5g7R/sUWY7r3yBoKor2BW5H82n1MTeDh8zKhrB3gFE7zJOXjIWPDi1WVx5PFz+mI0a8fDlBrWNGRy6ViglCk5G3zdMvz6iV/nUD7XWYWAsoE5tvvXeXlx98ipHD3/5vf8JoZ5t33n5Ez4mYnF9x+vIJ+Z032Bq8g+vV2E6AcjTrAlylqdDXG1A3QDiKqO+y9w2L7CigKs8JB3/I4d1b3Nnv8X83hgu0Bi2u3yq7h3tERwV+N2bbdHn6SkDaJWeI1jOS9ZKmvk0YSibLGtuUaDHl7EWHUS/jMompG81SCHr+Pkn2/NqG9xXrtYDcsGFnu8dUG6RJTd/cIjYsSj1kb7DLKLrH5Op9LjYnNIlF4YOG7QAAIABJREFUB82NTo/hjRu0yznLkyuO2SAMyKoYo1eD11KRsKceoqyQ/e4Ao804axb45jGNYzFnSWFd8qxY0AX2er/JOv8S7A2efoukdpnnMYNwh007w7WWeIaFsiP2hyazPMfSEblhUjk5FpIv0gQ72iLRNaE74P7OGzjFhm7Hhq6Pa++yqj8l6AaIumKWHBM3ENgOrV1wOPJJNwmuO6AWKV5uULlDLKNE6Tk/eTVBVx4XxRlGPeCdR7fYZMf86GcfEa8/5GjrT8kXHu996yHvf/yYLI9BaEwpeOPNI8qRxeFdn88/rphNL/jrP/8LHt26j7nl43o+y2WJkJpCaKq24e63B9C7w+d/8YK6gUQpZN/mwb0d/r3vv8usXpNGK2al4Nv9RyRJRsezrwvzheAacI0QAqFBVRpKg+VljzKtyBwwxZjMV9TWM0rzlDLT7JtvsUgusc0HuJbNxaam29nl/uF9nq3/JWXqsLjyqYVHcpby64a7/8f1WkDeaInSPeI6RYczbm+9x9Ms5vnylGxxwmJxQtNOqMxD2kbQbxtM4XOcvaRz8CbH6Ybp5IRGAzcNfKfAKAOE4SJEwGUd44+PSJMz4vlzrtQFhdjDdVJirbGK50jjPS7LC+LTlGcbydboeoiqLGeUZsTQ9FnPQ+piA/0aRxlIL2JgDPD7e3xmNBRVDOS8evYJqhEUvsu+63EySVitBOFwh10HBr0xq6wiLVIy75JC7+K6BwzDGnPwCW3a0OYJqXJQtYuDR6MySrviO4/u8HLRY3E24dHtfZQH3naf8HBMNi/Q6ws+N59ihxLHt8hyhUBydHSb3/2t3+PPL39C//4A7/mKIm3YTE559fwFt27ucff+Q5598TnbOwfItiWrG1bxmri34Zv/8Q7T54K39g7wwj3mcUJ+ZPNW7zv0rIDL6RlOc4CSAsux0YZAcJ01/bUUFSU0dV3x6vyMq6mmX/+AtZiSpVfXc+jDipH8Ppebp3x8+Wd0o10O9iwMw+B+1OdSwTJN2OSXSNPm6KGNyDK45/LlV89nvR6QV1ULTYxlLcjymHl+QXZ5wUAOUKplGU/QtYnovYBuwzweIxZzPnr1gje+o9g6aIjjjNmVRyTfoFErrFowuXpB7T5j3/0a66LEbgYU6iWrXNO1fR5tfZezqiIgp7EkefEZo94+fc+jsAwuklfsDG6TMcffVBwvprSG5CioiKIbkGe4hqRerbm6vELlCee+jSgaLmdzLFtQ+FtMVrBzc4/zF5/ROoJaeITUZPWaJKjpd2Jub2XM4xjBNvlKUdRrdqJbaFVQ5Cnn7QIDj7hZ0VEd3nvnbWZXL5jXM4hT/ubJX/G7b/wO4fgWwktp0oA41aimIUsLfvDdb9H2L9g3BFPH5vCWx7NPc7J0zSdPvuT7f/eHhL0eNx++idsLqZWirnLswGC8Z2I7lxidPbp7d9mJjnjbHPE8/WNOXy558vI5/XCLH4wO+Y0338NxrF9veIFWXId40QgtyJKU57O/Zvcg50Z0yHDrN4kQ/Ouf/XcI55QqO2LY+wFBccz86RWl0+APXvLsvKQ37LJYntOLQnru8Dqr2lmTFhuE8L+Sr9cC8qYueHb2MUcPBJ32Lv/i078gOVnS8zwOb93n8fGaSB/SGrC8eIFpF1TZBdVK8eX7L/jGrRtY5i22b1lY0kGLkFWWcW//tzjauUXNhNthxGl+xZ6xxVV5zqaY8eHlT1nZfW7etlnVnzMyd0lUStU3McWAXixIk5i8XGJbu+x0H/DkyS859wZ8rd/y8ckX3NjpMks1q+SUnUGHVR5DHkHRoJuWyhPsHXRQ9RIXTWp57O/uUllL7GnCxcUVd3p7yLZioDu8PJkT+AF26NIPJYu1xDF87nQPOF4dU9aXvDe+y6v5F0yymFaDKh1+c/frbFseGzNBDBpGTocb/SuCt4/I4xbf2fAXT3/EYfQdRJUyvD/g9NkleRHTbGI802YwiLh3/02qdEFrVZR1RSfaZuzu8yIxsLxdfCPnLP+YTvcHSCzWizPMVY+90SMyM8GSGtu9BlsBQgikAgRoFCeXL/mzn/73VHrNSf3P+OSLHnEasl4vuRU7RHc6DL2Q0HvEczHBdjbMVm8wb/4lRjKkRiJFSW0+Il49RrVnIA4xefGVfL0WkPuuR2VkaKPkarPm6tWCcdowHt6nv3OH94Y3iPw7nCx+jAgk5tYpzWrAN9Zv8ebdQ+pIYNcbfn76E57OHuO6Bul6wKRYMu53+Xj5lOcXKeX6CVkxY1qVbO8VVOWGKoOzieR4+Zybvs2bN3+AV7g8Pv4lSdnyzg2TSnboeZJNHmOOfDzZ5fHLn+CPSx6vp2zKDv6tDtNViHMpOBj5XLoOY9Mjq2uKLL6eyDNtbKMFWdLdvs/GPuPA26Y2LPLKoWuYmFIyUksq1yZrGvZ27jNZTehbLqk35klyTJJ+SVXH0ApcT9AZDwjbHbQoyRpokyFPXl1hmpr9O0fc395m3X7O5jzgrz/43/G6cPtwzM2jbabnFd9+79sMtkaEvsHRbYPFmY0q1wit6cgO6guTz9Ytj24qTuOfEHYjXpZDNnyCinKmucX0z54w6Bzywzd/F9OE/1O3IwWA/r+yn6XO+eDDltGN7yHVKS8+OiEYK9596z26vVP8YIXr5Nzf/bcg/IwXZzMMBVt7Pg0VN73vMklnFBsXoXZ4Ov2E8WCEet0HtLSEMAioyiG/+uQjZGqRtQHBIGCtvsDUW0xffMSHn37ASGSo2qR75yb9ozuYfZ9bvV3i459xQzzguHxGIHYZ7qa8zD7l/S/+V7rRDu/2Q/5qI3i5XIBRsGozun0Ps5IoOSIyfCJ5gMp7/Mn7/5R9K2a8fZPLWcVo18GUEE+mWL5EqBXl0xsszCWv0pgaB1EKRp5iv1NjNDWebyGaijRu6fQ7RNLhxeUEu2x4/8UnvPfDPre27hFo6GKzUhsqUZOkS361qhAbk54t6FlHdDAok5Jv7NylqI+5fFWiOgYeLYYjQdrM589ZFS0bWaJ1w7e/8y7PvvyCYj0htSOevzhhkySYjmL2UcPs0xPummPu3n7I3/mDv8/WVoihBW2g0NsjZucrQttlntn8ovOS210L7eV4zX3yi5pldsLJesNbt8d86y3FiZ1R6JrW2GDKa6+QgOsn+K9TO0JoOsMhP/j+uziqxzIeMzyQzNcxB0PAUpyVjwmyJZ+8/Iz56Ya08fjmjW2yxOMy6fJB8yssabNZPqNwUrZGb/wfzL1prGTbdd/3O/OpOjXPVffWnYe+PffrfhP5+PgoDiYlRZJpRaJiJxIsBUiABEmE2DCNJHg28sGBYkhB5AyIZcSQZcmmE4oSw0EkxUe+uV/P3beHO09Vt+a5TtWZ86FbAhLoiQ4YG28BhbPPOufs/eVfC2uvvdZ/oUspYPdD8fWRALkkQnomIKQWicTrhPUIiZxMOBOj0jsi7jmsFcIocRW3O2E6TdCq7ZGfSRG2ztJuniD6Y+JGmrjTIpUwOKg2UNV1wkFAiDQxNYo9sUkKSfZPT1jKLzCff4n71YeY9Sl6eJVIqsBeYxtn0sUXItimSkjO8ODWEZm8CBGIhy1SSRvRdNk97tE46WIYedR4AinwGTSH1EY9ygvztE+P6E89zqXS6MhsPz4ksZpna/uQ4XDIp5/fwAnbjC0NEuAh4bsigg+6p9Nsdrg9uc1CNkXLHJIJxUgrJSp6HcdxsOUEaUHi8ZPbaP2AlhZhoA0pZRK8vf1dQo6JI8gc9ndQk1n+Svll+oM6W8ljBhUTcRjlxU++xuxCDkUXEb2AiKHi+zGseBJcmzPJF9ne+g6yb1Pt+FzZOMfQbXDnwZsYskwKiIaTWAtrhCQPLeIhCgEg8me9055BHEEQWSjOs5wsMJUM1s5cRhFDCK6JMxhy7FaZi11m7IsE1UMaTybElwImY4epqRMTJJrTCWo+xM+8/DG+/e5XqTU9zkfmkVA+FF8fCZC7PnTGQxY8n3K5QC6SR1TiDCdDwrpBd9zDNkVc0UdfkCgJi1R7HdrWYx6PZeRuj5NQg3pX5GQ6gIjBpbkv0PS2qbePqdZb7PqbXBZifNfpI/Y1DjbHaHaP2XAOJa4zmSh09qv0rTFhcY7p2GZgu5hWAzceptE6IZxNE46XUI0ObsnCr4fAnTDtDTlTijIJLNphm/m1PPO5RbzBEHno8+TBDobkQLaPnZ5h5do6C7MzHHT7RC0ZQ3AQ1Sh39o/JF/NEgoDTnosa8anVjxiPu0Rlmff99zHdLv12DzkaJhJVmHgOUjjKsN5Djes4vQZdOUDyXEy7h4dKfdQjJuiEjThazGc2PMKbsxA+yHD5hWuIPGW7kgQRVZMI2RrxRJpJ30KVJS6kvsRXHv8OUTwOTm7ywvKneBi+xXwuRjxtUD0VCGt9PCHBQmEDwRMIxIDgz8y5//QSBAGKrHF25RM82L5NgMjiYpKb7W8jyGuMToZ0OKScydLK2oS6AuW8giRKyNE5nIlLrqwwDTa5eXKdmcw66a6BaXr4rv2h+PpIgNxxXWLCIkKgEVEtxkKfol5mpPdpdof4woBBJSCVLlK17tC368SjaVpDDVMIwB6SSqYQ0/t8LLrEXqXBweARstrktaVX8KcT7h7cJiPEMXsBYc1gPPQ4PTnEM2RSiUWCYMygZ9LvdZg512HSkXG7CmNFIhmNgKbhiQmGQ5fxZMwn1v9D+v0/YXbmBuVUgZPpTaZbEUIqjPQOp5MxFeeU2GIEaRKm1xDwqyI73QpiRCHa01lMGAycDpaeQAh8UskI9/d3+Yn1DUZ+DXnq44oqpjUgJxQY9kbM6AtI8S43T2+y5pc56u5hqyLpVAZHCBhMTOJTFc2IYQoN7M6AtCbS9nqo9Yc4QpJA0envqNCaUJ4tMrWecpCLEkiSjKoq6GoESVEQZZW14mXOHz7HyaDOk5PHGLEsV0qv8aj3Q1RX5+RwQCofw4gbxOUYniMgyRAQIIhP4+T4AogCghBwdnWVP/j6byPV9qj13kQ3AvaE79KvBQySt1GcZbLpS9hz9xg5GS4srPD+0Tvg+NT2TkmWRWjPIQYR3HAIr9fH5cN98g8/8H8mgiDogiBcFwThriAIm4Ig/L1n+kVBEN4XBGFHEIR/IQiC+kyvPbvfefZ84UetoSoCy/kEP3vt5zk7/3miUhzfkZmMojw/9zGurc4jhlw6LQnDusyknWY4rZLMwcbSOeZKCwztKW1zh932PdJJg5lcQMN+wPbDG1RrhwwHAsf7j2lWqry4tspPvvI5YqUwnmvy4PbbtKpNdAO0ICCkTSjMDBDmjlg8u8B8NkdYDhhMH9Ad3yPuLfD2g3+OxdvoIY14zCBQ4aXLP8ly4ZeYHBboeFViZ8cMnBq9ZpVBd4KmZNko5flPXnsVWfS50WxRjK9jCCHaI5n+ZISiefSmQ2KSg2TCYm6BTHSW1nCM1fPp+C3aYh89WkJU20QzAuXEIlv1HieNDnORIhIy0UDBbMn4vQj1mkzBPcOMVka0RKxeErtRpDccMLVGSEDgP432eQRIsoQoS4RjMQhklJDBhfWX6Yi3ESYxDo9dyuUVnl94BUny0BSH6TRFaBJjNLWZeOB5PA0h+iAEz8b4EEAileHM+XMMJu/TG7SIC+vElDD5RImk8jGkyAVGg4BC2eDaUpKHtduoMY2feuWXWZhdxbF7uNikk7PkNY1kLoMg/nhZiBbwE0EQjJ7183xLEIRvAr8O/GYQBH8gCML/Avwq8D8/u3aDIFgRBOFLwH8H/OJftoAoSJyOdhg6LeqjEWE1zdUzr3B7b5NmZ4/T2ibZVR/djhOTVpikWlT7TVpmGz3I4Y06CMIELVineSDTCx2SXLjPYBpwgEAmEUJFptGzWVxb4Fg3+Jjm0z06YioPWFlSSWdjNN27XPn0Co4S5eFDj6y/wVBo02fAZmePnBRiWvPZV49Zu7qEakLlpM/3tjcJh1dw5wXCGRXzIEyis06mMCQSC2NJCgNjF8cLsd1xKNSfUEgP6dW7NE7CTG2L5tQhHjWIuC41c8i5pTNsnu7zpHMfO51FdzXyhoDds7FFlawnwdTj+HDMWtnGMsHt+LTFHnpcpd89Il3I4GdVEiOb/mjM5dJztIX3aVsVXK+EFJWRFAHbGiLLCURPwA9EZElE1nVikRjTaRtJEpiLzkMrR63VJpfy6HQ77B/usVvfIRyN0rEeMmhIOJ5FQMBwZBFJqOAJBD4IcvA0pmj5SCGJbDKLM7IoR89RSg/pVhVOWx1mc1FcO0XgdBm1PbreJo4mEtezvLnzHZBHRNQ4l2fPs9/pEM2Gsdp7BPwYhczPurn9WfaL8uwXAD8B/HvP9P8UeP0ZyH/22RjgXwG/LQiC8GyeD1nDZ1l9lb2TGvd3f0BYcrC8KGnVpNV7m4Z9Sn5YRJKbtE2P7d5D1uQ5ZGEB2w9IqFN8e4M6DZbnNMxgwtTaY2qZ5LIG7b4LfpRIfhFRAkUKc+v4Bt4oTDDV0DNzBLZOfTuM0j7i2tJVvFKAHi8xFPv0+ncID1wIZShnzyL5VWqdCc29NrV+E9HTccZRvlN5i3gkSTRqMHQDGHaJLTaQvDliE4vTfVhaWqerdth8sk9ylKeSMhDUMYVYhmJmjjv215C8LDFpDn+6TX8woNMec2Z5hc2DGhH/aeOw3GIUP0ijaxrv7n9AzwY5kBDHFn03YDYZ57TdQgx0FmZXyMg9fAnSkTyxyBqnF/rsjR5x3DogbmRwETFc42nmIaAoCrKuIcoSASKSKvHS8xe5fvqHDHvv8Y13bpDSdGYTGzzaPCC3XiCR9olFdSQRRvYEyQIjpDEdWIQy2tO+z30LWdZJazM4iokP3D5KE1PmyEabPDzdYbFvUncHFLMXmPo6iZiEGRhMOi2mvsNydoath2285IiscZGBEkb+S+rf/nU7MkvATWAF+Ec8jdf0giD4M26uE2Dm2XgGOH72B3EFQegDaaD1/5rzz9uORxIqH+w8YDhyabZbzKTnOHEf0g5PyUQ3yNkaIXWKOM6QTDzPyLQophbxpSht65Amffr2CNvtoudFVCvO6GgDa/gYN3+TcKREPLTOzfv3odslXhqQSkUpJTWsqU3IHuHpTZ67pHFkWrzdfp98SqHZ/Q4pI09E9EnMXGHnuIsVuU1vPEXRjrn8qkfbitCu1Tm6IxNPl1AVB8GeYg77TDyL+LyCOa7imlGMmMnZwgoOEgd6FVFdJJEqcvf+NgPRYT6TwQ9kmt0uf7L7RzSenDJGZhrK0bF8tre2SIaTLKxHmdQkwsUeztpDcv1lSgtLGEaBozuPSWQaSKmHrAUbhIIiE6lNSEhSHYyZanFGaoH6eBNpHOedN77HmV++xKhXxzOyxCMJFF0EQUQL64Q1lf7IRlcjrMeu8q2b30KaDGjUAhqRGJlMnfHQZfObO7z8uVee+vZBQEQP/zm4JsMBSiyOoqkc371HYWOJ1dIKIz8Aajy/eJ4bB4dUWwMicYW+0yMSRAhnwsj9Kb1+gKofgxAjrPZpmR6u1kWaxlEDkbCRwwt+zI1nEAQecFkQhATwVeDMv853P2LOP287HsmoQf20jjv1uLxeJp7SOTqYEI2tkzRmqUxaNAeHNNtNPhN+jpA/z0nbRpa69GoBSmyKYQhMgihOw6Bh3sFz97kwk8IKRMKhPrb0AesbXbr7KZSeh1WNM40KKOE2lhLlfOoqAyVgxVHZH7yFr0uI0wEnRw1UNUfj9ATkCZ4l8tzST/DW1jd4eC+LHGugxGTWnreZjNuE4mcYjk+YCatIYRnbk3DMMKeNfdxgyoMnP8SILKKLKZyWxf70DRYWsoy6FY6PK4StRZbkGDeaT+gEoCkBmYSIp21T/pSD0xzRnfr4yhHmsYnW8qB+SKK8wAsXLmFoEn0rwJYGTM0Gu7U6vb7D81dfxA1F6NTrjKfHBAOVYW3KG9O3+fRnv0g6UcC1RlghDZUIoiyhaCrhaIjt4zrFtMpyfp2C/jwN06KYF+iOj3BwyZSSHI1q2IMAczzBHvbxXY9IIYaqq0z8CfQEUuk0XjClubvDZuc6S/ECw7FBvX7EsDlm2OmhynH6doAhOBw/+CbJuMy0myQbn2E4PmFkNlmZWQcxjJQ4Zm86wPc6qOL/T+RCQRD0BEH4PvAykBAEQX5mzWeByrPXKkAZOBEEQQbiQPsvm1fTIphSg40Ll8kWB+w2jmhObOS6T6dzwub+MflVHVkdcrP1VQatCHMhhYX8c1ROjhiP2ziRCdsnU/7qx3+S5bVfY7f6FvXRLbqHAUVzna4MxjBBRAoYCDJB2ECLC9RPDELxgJF6SDl2lc2Tm6xkXuPqwqf56ve+wv796yRXeqwv5xmH97C1KXs1j6Q+odHs88LiWSKpR3zwJOBTCx9nqz3g3s2HlDIScnwIkoY5EnGtFI3tLl78kFK2T8hfZPVqmTe3t1CY0vVd/ME21YZCI9oCyealzy2iLteYjGu06+C0bGIzBuJYxPYKxI0W3nSWkaghOD0kZ0g2ptLu7zEejnBtl0KqxAtLL7FTv0FAFsFxOd7Z4tLiDPn8Kug+b3znq/zil/5TrOkUYTJClTVEWUZRJLRolGjEwHE8Esk0F+YvsSXu4gyGtLZGyIkwsm9j+Ra71V3ef+sNNBN0LUJBOEtgeaiizKjaJoRE8fJ5Tvfv8JXvfoXHzQb+WCCXmcU8MNG8CHXRYiVZxhZCXCmsE0kmCGYVsvoc28f3qPe2MN0hvjNmppRjq1lD7JaYOj/GYZAgCFnAeQbwEPBZnm4mvw/8PPAHwC8DX3v2yR89u3/32fM//cv8cQA/sCnMlykkL3Jcv0FO3SC5WuVsagFdu4gbcdh8eJNwKMZseYa6M6U+0plY79IID5lRI/TcNGeWZthunvALr73Ei5f+M/7Z934PzT3BG3UIZRxIz6JHVcrGGexJHEm7wVzhLHpU5J0P3uV6e4eDwykvfzJCKT7GVQ1CxRGBMOTx3hMmYoxSPsrAO0L0w1xaL2F7Kp3TWSL2CX/8g+8Rii5TTl2kM+giTj2Wy0sk9CE7xycYaoCeitIXhijhx3Qcj5WNEI7dx3cT1BoucrbAyJWZW5tnef2z3G/8PkxUgmaGaf8hMb2IndhFlQUG/Q7Trkh5PUzt0OR3f/+3mZvJcmjbWK5IOXGeIDJH7aBD9VRmfiaGEZJJp2WSqsXO3U1a9REnmYe8+umfI5PI449HqKqBL6lkQxpqKMzibJaxaYFnMpcMc+PxfayGSBCNUMwUcGSJNQP0cInf+9bvs+qH+Mwv/AqDTo9O9ZhMJooui7zze/+C1NlVhtKUitvHljzaVpfOnkU+XiCXVdk3m9R6E6xRh/rRCZl8idWVVb75+A/xU0OyOQHXkrEGAfsnGrRjnJzU8N0fr5C5CPzTZ365CPzLIAi+LgjCQ+APBEH4b4HbwO88e/93gN8VBGEH6ABf+lELiAgoU7h38D0IHXNYBUOb4UokQpsbDKdb5NUUSnSeStfE6XbJLz+HrnjMLM/SPt5nxohxYt7ANhr8yd1/QtIQKcbidHUJd5RBUDR85ZhOt86gFyUXfpX5+CXi2SR3u+/i2z6Dbgd9EuGt/+NdJm0fLSHgDy3s0yxiNCCdj5LNDph0V7H9GEeDHTwvQBoV8cMu8dSAcj6ENF3mG3+8T6oQwkxPKa1J/Nz5K2zu72GEz1Hpfh9fa/C40uRS4VMY2RnMcz1S8+DbPZxpnWFlwObu/0lT3OOCeA0r+QHpiEbffIg4SqMlZMJSivJZCUs2ycRXaHoDnnRrrG18inAATquP6Lt88P3vQ9nAHInoSprXzpapHDwhko2gGgalNYVb22/w6Ss/hygKTMYDRD/GRFeRFYl4Mo4kjRhPXM4sXCZ/44f0YgJTY4BniAzaIwZjAZcWB70atSBK+sldJkdVmnu7hLMpMjNFNn/4bbK789Q1jUlPx5kEFOJlnHCdcKiA6IxJWRaEROrOhNr2kFFvSLuxS2nuLK16m90nHbJnDWbnoni+i5KcxWs8QP5xeFeCILgHXPkL9HvAC3+Bfgr8uz9q3v8HyAORUNgg5ss0pyPs4YCNuRfY71t0HYkyL/HYPiCe1Qk6Ft1uk92dJ3zhM3+VpB5AYNDfOyFhhNkfq/S7DaT0OqcjhYXZc5gxi+lIxJ/skIupfHBjQj/7LpVGnEvLn0BVLrO43Od6Y4+FxTidjk+ndZ315TSXVi9zsr2D7XcYCTXu3I2iiyOen7vCxLvI0aBL6/iA+fUQTlzDjaq4SoSrn59jMDpE05rUTwZU7U00VQNli2lbJxF/hShTaoM9los6seRdxtOAiS8iOnNkCi3a0xF5cRZTVWk3YDSWiERjZNOXEcJTXlr9JO/tfJNxr4lAh5FzyqTpcjB+jBKVmS2v0n28TTG7iufBcHJEIpfHCSmcOkeMlQizG1H6Q4FbD9/j0uxLRFN5EtEMU2uM5YbQNBlBVwk7OrIYEOQLKLrK+efOUsz3uFXbwe8GbJwXscYu/dMpE13h7eq36G1VkZo+TlfEOZWo2TX0oxaibrCY01E9m8mwjugqNI7v4QYxUokIk0mPUraIP1EplsMoUgTDNgnSCo0nCmNnSvFzCeJGhs5UIDSUmDofvvGUXn/99f8vePw3Ir/xP/zD16++ep76YIgUOcTQJJKdDPVWg4VEge7IY0QDWx6QFXM4dhElCDjob1M5bfPHX/0jjJTA2DZIy2dIzZxneWYNJTJhMNJIZ0bo4VOOjjsoRolmO0k0lKFhdlDFEZ7rsr+3iTeSwbG5/FoOUxQIeR6FeJlJs4ignmAYIu09i2HFQLaT3H/rgEF7yNoLMnMzi8zELnN60Ge+fIZmtcvm3SPkIMyZ8mvs7p8ybkeZjPvI+irhlM9CeZZBo09ts8XJcAyOzWz+HJGYihneRRd9opOz7LeOUPQY6dACek7GkUI49oD6aYdBrnLiAAAgAElEQVSf/cR/TmP6HpFIkn7PRCKCrPnEMkWyCkyGbeauZCilU+hunEfNbfbrd8HTSWanmIMOYnpEOr3A9uMtzi6sE0vm0LQIsioSUmUkQUJCQPI9Alngmztfpz86ZL+7x0xknXZrxL13D4mECihZnTMxla29MQNhQkOasHhlFS80IZQO8JUQYrhLTJCwDJuoHsYaFkmmG5x/aYhHhJk5gfrIJSamIOZzqXyNSm8fV9JZ2Vhn5ZyH0i1Q6dnYpoXgarSbdb78t//rv/cX4esjAfLf/K3//vXZqxnimoRthRDEAiUrDraOGAvjBgMScYVxX0QRIyyvbhDLpxHEIZpiIJpRhs0u5eVZfN+jN66A77LTuEFEm5JPROn07rO16SAoJqG4Q0bIMB7V6Bz3ufnmHtOJxsXzV1lcusbt/R8y6PhoUoLaicnm9V1amzKDephxT+K5zztoqSlCeoiie7jOiOO2Q3fQ59LiNVLJc2wdtYmmXJKrx4TlgIBZCktnCTSXl1ZydIcP6NVlRFdl0JmgxhLEU2mOzNu0D7expAHHj6KM7AafevGncKQKiurguTGelwKutk4ZhCucmDukQ1G6rSQJTcT2+si6j+1InFs8z93mdU4aHcZiiGpzj6WZOYzwPNV9i3Julu3bLcxOjEKhgCf0kLwI+cwMuWweSZWQJQFBFJAFAVkOkESBvdo+k94mCUelP9BYWMyxvvoSRiTCpHuCHrexrBAHj5s4aCRyMaJGnJFno4Vc6jt9fE0nbYQwRz2uxEr8pDpDTgpzq9qi1gp46fLzzKUnHLRPeLR7n/RCiFzxGnr8hFvfrRH4cXqdFrNzGZSoSPVRk7/zt7/8F4L8I5G7YgZ9rCDCVHiML11gPX+BIN6g9fBdhuImulHm4cEOoVGBdM5GNgeQuEnnoEngypReCihp19g6NDG0OpOxR2peQo0XOJ/9LNcr/xsxZYPzV0y6AwfRs8mUdKLFPAmngDetkRViHO1U2OOIXjNFyNdIFCSSuSX6DYdxs0tn0iUQw2zfSlNMuSRjCszNsDaf4Orcf8ST9h7VW2/y/be/x0TqE1eWaG2m2OrssZJfpzXcpPp4h3hpTMUb4gUHzK2GEMQjAs8g6l1ievoKd3feYa5tYNZ1nHQHz3N49LhNeQnins1DM2Dk9klX83zt6B6JeIhS+nM8vPuAbCnEi1e/wJ987w0OajeY08O4dp6e4DC7uEioOEesforxsXPEm21KZwpoooHV75IrLxMyXNqtI4rFGXQ1iu34hFQRSRZRpBCS77EYnceR5rl7dIoZnDLyTpGNh0y8Gj4Sxxaky2VKvomulZk29ileDJGYyZMO5tHN25TSBuJEx29O2Qy2Sc1oHNUGeILMsC6w9WCXxdWA7Az4soItjDEn+2Qi8ywvRUGzEM0EkXyOSf8QUfzwDJWPhCX/B//w77+uzbSxuiFeOvtz5FJRZHuHZitgd/sR1d0GaWWOM5fO05UGLIQNKr0t0sWAg1YbuRdDSw7pBrfoDAZcLOdRQwku5l/lq2/+PlubXfotl2qzwSsbZZyoQjI6JHBlKr0ws8kc+3undFse8ViO+fllVHGCnRxydNAnVQiYeiZyRMGtKgSait0X8YQ4ktHnqG7yxnffon5Y4823b5BNFMkV5xizy3g8QBJUhmObXHqGhLFETzpB0E2Wr8Yxgw6nO2mC5EUq/Ru0Kj10RcB1ZRbyGkZilua0xvNnfprH1cekxSZN16A6kelaE9xomnQij+94ZMQIbtjEssNMmy0CTWMSmqBHQZiK1PsWxXicpKfx+OgO47pNKDODpLlohoU7CrO3t42pj1jJn8UIxbBdh5AiPytEDtBUmXt3b3H7/j32Dxs0+0PKa0sUC8vUjivkIlHS6jWUSJPZYpGhU6OUVRkMfexen34wRnNCHLRtHNmgNXEIMimOxC7jiUxieYWrq5d45/q7DIQj8ssqyd4lBFEjqqvUBjeRNAtZTnLnnSdokSwxY0xls8bf+vW/+9G15O5UYdqUSWR0Huy+TX3wPvPzWYikyTeX0ctR7EDkxY3XGHoD/uV3fpdRX6N2MsEeCogzRwysGZBK+MMh2/E6n579PP/4m3/AqNXEGqq88GmJ4vhn6CtNvOYtTlsThLDIxy9cI2wsEClDfbdLeTFFa1RHDSJ0Kz7R+DxzV05Jb3Tpd6K4+WUW10vsV94lqsew2wliepRP/TsvcnKyST73Eqqa5voHN/CTUSZtkZlCirPrWWqOiRDug5XAbQ0Z+x1CaonPfuI5zs1d5iv3ttBjWe5tbmO4LsTzGKEIESWDpjSIiy5bY5340KTragzaFiO7S6mwxKCzj5wSCCklWpU66dwCvmpiBGFkVceKSKxqEo1+h8TMIqVxitCZEuZ4gmk5pDOziOMQ28e7bB+eUm18hnSmQICC4weosoAXAKLAc1de5n/6ym+xfKlEq9qiM26Td5LEMzkEa4QS6rF3MsYT+qhSm/22i0MCtR9mUY7xw7ffJza7RD4vE/TGzGaLRHyVJ4OHjOvvoKenqBON9hHkE3MsnbnAduV7hIJ5CopLw2pgCsfMLyu0d3cRhgmsHzOE+G9cBBE8O0KldUrEEejsuoSNKoncEJIW5dkmzeMsf/LW/44akUgX5plNicyWbzOTTdL3Exh6nPq4inWsonsl2v6Aj1+6jBJbxm33GdfGjKN1ypkylnPEad9g56DJtHfCtUt5Kic7iEaYw6bPetjFW0+RjjyPWDtl8zhKmPNs3zti1HtIKuuRDRXomwPGpkBg9fj215vMZnPMpePYTkA+leanLs1z3/uAerDLaWwP1VbZPxwy3IkjOBqxmQLTaYL729/iB9q7JJYNptMmqh1CD+lYwxV6j95HLc5wpzlEVBUsU2Wr2WexaGDOxeF4itTVGQnw3GyW+49MhEnAqb/NLEmqnR7pWZW8nKcdnGK6sxy0q8yVClT7DrXuPuuFDG7XYH9yF2NxgmTIbFY+YGnuLIlMCdcDX35K9ebaAmdXzvDFX/hrHNX2UZQIplrjtH2bl5df5Nu3v0W7s0NSeIGN1Q1Er868NsPetEYg9WkdDdCzIohjXGR83efJ7luMBgGziw7dRghbSfIzf/0LXP/giGAU5vj4TRr1Hobco+FX6bs2+UQGaVYko5+hsr2DxIfzrgg/4pzm34rES1rwqV9bYzSqk4rO4o0DdvcaFNbDfP7Kf8B2/x0uFz7BzScPaLlvk4omCSlnuL2zh5E8Yi6RYv+0RdbJkc0V0YMFIuEoDwf/ilzOot4JsRK8SjV6H8ENCE3Xee/eXTJ5G0NI8MLK3+Sf/eE/YtzqoYey6FLAy+ey9EJ1okqWcCTHuDWmZ3dYuxgwl3iB0+MTTnenqEYcM7ND5UlAJBGwlH4euy/RPHzC8f4JGAZTWcCU9lEzYJ4KLK2dZTV3liP/TfYfKwhOjOPWDlpEIpvPEJ4I1J+0sVyZ1fMXGE8eMA13MLQ5JrbGZDJB6AnIGZ28ViZeKAAjjtrHBKM61kkILSKxspRjv7lFR2uQtgwS6QxjQSRllLBGDUQ5ih6aJZOUCZPDdEfoaZ12/R61Zo0v//V/wuLyeWRZJKKKiD6Ino/k2Ow19vjy7/86ITXGcev75Iw5+tMukgT6cJFoxqQ1PGR4pGHVopTOLVEZP6D1qE55LYknquSLq9y99SbO2MBqe4RjHkvlGMO2x/IrF7nz5hZ6JkVstYbgriNbAtWHD9BSUdYvrHPcuE9UyzBTXuDb/+uf0jjp/oVI/5H55P82RBA8svNj4qUw4aTM7LLC0pUEpfQq1dFN+p0Qv/f2P6fm3qXfL7K3P+Dq/KukQxHi9jpTq8xq/FMEQZzHlRpz+V2cyYD+9iLByQtE/VcZiwqGlKdmbeMqFi+tfAx/nELzykxdl7/xU1/kP/7l/4JSdpaVC+tkNhY5f+5n6MclQnIWZdYjF9Op1YcMpQWC/JCLF5aQaCOYc5TXPksoXuL+1n1yswn2Uo9IfxLS62kSiSipeJaZ1CUENcyw72HMqnjxI5YvxckujEgnA3JRm2nfRM4mUXSNa1evsbBeprh4ATU0TyAajGp7nDVSoClg+9iRAY/f/i7m/hMSWpxcKktyJUo2O+K4d0KheIUZ8Rrx7Dw92izEshimgD11UaQY66VFUmqS60dvEg/L3Ln1HgfbR7imz87+E7BsBAE8BOQgQBYEJEkmn0mRtjKwNyTMAr1ak0xOISkUOOnU2dvWkIQNMnPXKK0sY7baRGWByDnA0GmOOvQnNgv5DSZ1i0sbZZ7Ll0nrMsFcivHpkGDoEMtGEXWXw/u3uPXd60gJnURkla27FSKROGq4RZv3cf3Jh+LrI7Hx/G/+/uuvX1p+hfTsq2zX3mSpMGU2F8ULJE4G15GkLPpkyJNHDVYSz3NaafHVb3yDfrtC1BoxVVr43hwt+x5z6Q16Uo+j4xjD/oCtrTq+bRM2BGaLP0daSRIWZE69ByymL7F1+oi5bJGZxQUwwqRmRQr5OoJQQNAzJPQrNIcdFCnOsbcLVoZBT8HsP2QsxGkpHcyqjJ6r0PZvUzlp8bB3A8V2SRfyvPz8r1Bcv0Q0HmW9vM6gPeDaxhnC8jyV0wluS6XR0vFVCOdMwpJCNKSQKft0Tk44qE1p1R4hiSHkUBS3IxHRXYhMOG01kemxvPIxMrE4tVYfNSZhmU269pCIoTE2O3iBi+cM6AxsOsMKQ8EmIqeIxgz61glv/fD7DJstBKHD8up5Gr1DPLeHNzK5vPEqoXAEWRAQJQExAEUUCBshZMfk/QffZaBZCFLA5eIvYlkJxr0xEi520+X4eBctJIPRo9OyWC3m6bUkZCfBdDoiPpPl4tkNElmLJ7ttZlfznBwNyMwVCAkxcimNbCLHdF8GXaI8p1M/aOM4PoKbQ3dE+hWoHw748t/6rz66G0/Bl3n73UM2hjKBl2egrHAUepPa8QH7T0TWXn5A15riWkUeVfaZTkSKK2u8cFlm90GLxo7PqPsBouaSuajhK0VKhVmKeYG37nWxVJFkIspxew9d8omqDoJoMFR3mLs4YS4jsNt4E80ykY0ED+5XCCVNyorLXrfOtfULPD5+Dy0oMpu5wqPDXSLzIybBJvW6jayLCLZAlmvMnV1h6u1zMN2iOtrnOw9+gznlDN/8+pvMzS+hh2U6wzbv3d3DFjrUBsdkUlnOFj9Jo19Fim3TnzQRAoFmX8Std0ivlMEcYeTD7OpDzK5FrphHMWWsIELV3mWUE9GzEUJBkpHcJh46B5M+PbNDNCbhYJKTopBJoJolJm6FfqtDvzsimiqxnCyw375JX9pkcfkalb1bWGaDTueUeCKDI4toPC1hC/wAURC4sH4ZNZIk6jeIexs8evIEs9Fk0D6lM+pwdeMisfkZXHFMtTqlvJyjH4SoVPcJGwqLq8tM7Q5OVGHrYMDKxRVSRYH5UBvRd1hcX2GpIBOPSUzW2lTeGzMYdHjhizMspnWO7xroORm4yqO3fu9D8fWRALkWVnntF5Oc7MYYVLJoGx/n8Q+qHOxt8vLVBIMDn6XS58m94rJdlxD8A1bPrCIYXVauXiGxfUQnZJFfWKTRuk060PAjD8nHZH76lQiBGEeKPWTV+Dw/2OuxVf0Wrigg9eKEvAzhdAl3rPDDO28RjD5gsVjGn8Z4691blEsyXu8cA9PGcgdcP/4aMb1A3xIY9dqk4wVSkkplV8ZYHWHbIlJojRdXkpz2J9y8eYfa8H1iWoaRmsKXjtk6nVKcmyURvcCSX+TmjfvcrP0pggjhCCghAXkcYjVRpBm3KMzpHJ12EbwRYt9E8mWyVy9g6FPKkSx7p485E1vjhwf30NI+I6GJ3JqQzl9AN8dcXvw8+0ePqE8f4ff6CF0BWwxYO/MJonqTwKqzvXuLUDTDuFvlVHQolFa48fAhL1Qeks0vENVSBKKA54MoPLXombkFInIBz4vieGMG7hYRv4AXmbC6NM+5xUVMRDxDpBQ1McUai8llFkN5XDHEgyc3eO7ac7QHPYREn1QiwsngKaPxpdICWVfnca2FEp/hi5/5Naaxf4wurRFxCty5cRfD0/jWH7/Pcm4HvA9vO/6RcFd+4zf/wevScoWZ+SiuMKV+UmHvwV3iSobDmkmrZXJ25SJKskjb+2PMfpzqdgWaES4XN8isFZnJLrG4lGOn8X/hK4fo6RZb9ceUpzIPT4f07GM05Sy9kcr+wwrpcBoFCVXOce/oNjMYDJUodl/C7w6RVI8LF1+gNnmXk+kug0kcUYGcFmM4dfnCtV9hc2sHgQ7V0xNm0klkxWTSKVDptam3HqA7ETwzhxpvcvEzU8bdLtm8gG1G6DZN9rdPWIqt0uq0KZSTZMNRgpBJTPcILAXVVTh7bp2902OyRg7btlkprWL1ZOLJHJFomPfeeBfLsQjFCsTCNrbeJGZYyOYFrly+xNrCc+y1bnJa36fz2CKaTGApQ2JyCEMJUGyX3eM95KmOEjJIRxfpto4g0Igh4XkO51ZeRNFCqKr8lLdcehozVzSV8uwCX//ON3i8tcv6i1mkwiGTagFPFrj/wSGPt+5ztLvH2vlVGuNjRr0K1UaT2bBAdmMd0+xQOzlhPp/jg60juj0wFJfF9CzVwwoyAdV6hc1ahbydwh4EPKreou/V6DePWSys4gshTvcafPnvfITdFSMw6Lwd4F+4RSgaolD+LL7wKfxA4XT/hMP3HvBH9W/z3Mcvs9Vy6Y/uE1Oi1IUp19+8y1/7pS9y+dJLVOwhZxZ/lcl4zJ3Ht/CtCtWleSYTj95dAZ8bJNUVXljeQEhEuHL247xzcI+sEBAKQiyFLe63N4nMJwlGae7e/1P0yByJVIDnRRFVl53mA2RhlqkrogQqM8ansYhiijc5eGLzwmqSVsVk6llsHe/z85/8m7yz77N98AYLayVi8iJCeBOz32Su/3Es9ZQXLn+W93e/htkOEV8aMRkoRJUkHd0nPuwQMgxO9xrEciFKi7N0elXcUZvHR3VKZZXaoE0ydsLdnWPknElgD9DlKdff/wHLhRI7Bw9YXXye1USEx40qgVZl/16fdsklHNfpHvXJn3seRJfjdgPZszk+2iGUV8nap4xHfVKFEhZPOXIQg6fsQb7P/NwM4bCLKMuM9sCKzGBrY6xegO1bKK5EUcmx+eQ6A7eH4kpMhCGdkEHYy+NPZXxDJxldpJyfElZNkqE1GmKazalJeLKNEorQ6O4wv/rTLC7G0DoqohbwZPgET/TR4qG/pMLzI2LJ/8ff+s3Xo5lZdD0guuCgajJEOhTyIZbPbZEpbhBPqdTtAeFMEbM6IRAjWKaDFotRrVXoTU+Yiy5zeHjAw+vXOb96nlRikX6rwdLsefRUima3j+v1EaMGjmJx2q/RG08oR2bZ3n7E5LTF6tpF+lPwhT7JmIioPz09HU996rVD0uoaM5l5mv09TLfJyOsTm1WQgwlbmzXS6Q1Ox2/hB12yyjq1/i65eJhh08TXT6nVAmRFx2eebHyJR5UHVEYHqKkuhcIqeBZxPYZnuzRGDrYwwRAzDOwuk4aDHZ4ys3CNw/3bRCSZUaiPFulQP3CYBAHOYQhhEKEXPkKcTDhpnBBOeEiJNu0jD3cKntxGT6bQIwL6JMXyQprwzCzOuMNM7iK98QkIAb2BTb6cZTZznsXyMpIkIAGSICAJAiICuqax19ji7uM7mJZKWDTADpCNJqWZNKqcYaJJDPsDsomAfFolkgqT0ed5vHeMa7o0mhVCgkpUj1KejbLZr7IyfwUhOmS3eZtBT+by+uc4GDexjLfZfdylFAqTnJ1jd3RCEpvjJ80PteQfiRBio9NmeNAlmCYw90sIjSh2I0b9+IhKZxat1Ob8Jz7O2Qsqs8sVXv1Cgsuv5rj6aZnSik/+XARzZPOtH/4u/V6Ts0vP4Ue+gZP4Af2gxX7tPvfvvIVrtpDEEbpoEZOidPdPyfpT3vvBH5FOxDCKElKkTyKnooRmmEglrp39SUbTJlkjCg4Iio0TRHAdjZ849wJq5g6+/FVubt/FDWx2mx8wUWzG7QBNM9EDH8dpICphmoerSJOzpMQvYPTWmTQd7IqIVwnR3PY53dmmfgQvrf4NJK1ALlwkCGRq1hNCSoJIMkQqnGHUOyYqpEkoKuHQU870RDKF4sVQtSReoDE9FuhWJQYnDllphoM7Fq6m0epU6DyaEk2NmQYmiZgAIZiRHMQhVHt3mTNWcS0ZR4PKzSP61ja+6yEEAj4CLgG+GxB4IPkiX/qpX6W8UsQct3B1GA6aDHYjjFsu+sIx2Rmb2GzAJBA43HPw+iLTSg89B0ZxRGlJQs7dwcsdM/a7FMszCJqOZ1YRzRxiNMTF80tE525ysKXSm445PmwzOLhDTo0wbPaZuh/uk38kQO54DpVeg4hmYCy5iKU9SqUR+AU2sl+C7ot88LUfIDsB7e4x9z9oo4g7EGkiF1S6nTalQo5zl17gYG8bX5fYKP2XXM7/Ev/+X/m7ZGcz5FdmmJ+9SL3ZoNWt05w8om/u4SiPWbiiU5jLMU08YBx6n07vXdq9myCE2NvbZlA/RFS+z5kVm1S0StxTOT3c5xu33kC3XiU4OsdS6GNMPZ9afw9naJEIK9hClRu199ntPqBe32dwXIVA59Hh91lKZLn74JBqY0I27xNx80yCEalkgf1RFS+yjRg/xBlDUpeYdDyakxrLZ16i1Wmxt93jeLuK2E2SUV7Gw6E/7NEbNWk0Ooz7ApbzfzP3pkGWpNd53pOZ9+bNe/Pu+1b7XtVV3dPTPd2zz2A4nAExBIEBCECySNohmVaEbcmhkE3SssOMMG2GlnDQsiQuJgWKBEWCJBZiCAwx+97Te1dXdXXt2933fc/FP2YcoR8YmjbkiDm/MiMy8tcbb5zvnO+cp4HgEbl3q4DHtUQzc8ja4jIWyYmuDaA3YGPzLus7dzg6TTE/G2TO66egpZmMB3AIFkr1Pt//1ssMtd5Ha7AE82MWkcBoZGAMDWbC01yeeZz+QKfbTjGwS0QWXahzXXojmXRzk1qtTa/tRTNkqnWdo/4JZk2nPxIY6BL7BwPW/6rE1bfqxOQkhY193n13B1G14wu4uV39Q0qZPkU9z/LCo+T1Jsd6jaOtDDPnn0NRXJ+or09FTm5RRVafj7J0fhx7IMkrL32HaMJCr3PCW3/xbQrpbRyRAW/+qYjmtGNXTHZvJxk1PqqzRvxHVEYm006TF774BOHkERZDodOcI125Rs+yT2fg4qiyjS9iYzwkcNyG6cVLbOauYDXqOOQsouKgVuvQK4/jM2FQOeCD3XfwJPrc3ijiVNw4HF7ssXfo9wacmXqac0uPc+32P8cXCzG4ZeAJWpj3PsjKmJ9vvvpHaIqTk3YFr1/EpUSpdUeUr2XIPnKF8xct7BeDDJ0aD8Y/w9b9DUzDhtgdkTlogqBC38lo2KVj6BhFJ4eHp9y7todF0nC5EtQGRRqVQ+zVEYYh88DiPNvHO8h2FdGw0x11WHjyDN1eD61p4/76OlaLDafootSu4p1x0TruUus36dULlIttpqLLHNe30MwRPneA41SZUqWC6nAhWKSPhC6CgQS6CQONxy4+y6s330SwuAmGTdqdAjvv9phdnKFb7NAYWBlUj/FanMTm53FIDezWOrWejGRRCTkfpVDeZCDpHL2/wb1UDdXmRdAseANl3v5Wm+mZBaRcE9+ZJCuBHlvp77PkeoKxsQUk64+5kuL/77BZXfi9cf7qB99DjltRPB5KpQ6NShWHtcHYZT+iHiHm7HDujMzdXAO7w4VhLiFZDDL3tplYLlNtHwBWWrk8K57P8erGv0XXi8xEljg7v8hedo/TzAnHxRu4PH4avQKKUCc5Y1It38KjjKOaq6gTA3buvMZpqsPEpI+5+ae5+faraN46Mc88WqvB2sIF2m0dY1DD7X6Uvb0Dwj6ZkDOA1dmmNOgRczjpuq1kt4Oktxo4fQ5stmN6FoNWR0VwD1meneSgcoPN3A1UZQY1ruF3hvE6YtSrQ1SnSK2oYA12eeqZr3IvtY6OhCh1qWYbKG4XzXIOUzKYm19C9EWZpEl0fIGk28u17etsXPsAjRLuaYOLlx/nkZVn+OYPfg+HX0MbSsQmPFjtMSSadOt1enKbhbHLmKKFxWScSq3JwcE+U2NJJEEE00QQwCJ+pHGLIJGMhDkzf4lGr0FMk/ju9X3sAZlRV6RXFJGxMeoPWH1yDdEvUChk6TdH7G8UGXs4SnIpyRfiC3znG3/ElQ/rRKIxFmcCDENNbrxURsDPvdw+pCx85w9/k3NfeRKvuEZbrGDpjVBtzk/U16dC5EgG7sQY9koNS9eFGDKg3CFuG2fu8jjT448xlxynNTT43T/7PZbPWZhchGFrlmS4Rfex/wpjZGEztY/N3UFvzrJe+wseDHtZb2fRrCc8c+GXufbb/zM+d5LUno1SKY+q1JH9BlLTieEskvEdolfuMhZP4Lm4zZOr0/QbOnduXWEmPklPLpKqlLCJLgxngWQwzI0b3+fq+hGKKrP2iBfbaIahP06+m6bZGSFq49iMFjPOKHa7g/udAjNnZjj34JNk0veo965SOTCImg3CYza0YZarlSs4rRZK7RY1s02x3SXkcGExZbZvbCBp4HX7kZ022gMduy+IKJqUS0fIkRir/iBv3brKumoijLokEglGRoRsJcVb928zG5mh3/WjSC0Evx13IILTolDYrCJqHu7c2GOx2UaNhqkrLvzeEO1+G8n8KGPRzY9WypkM2TzcZm1+kWa7yxceeY4bm3c5PNxmbCJATSuTu3eCZrPSKdZwhpzcv3vEpUfOsnM4wjXmJThuZ9RqE5LCNO0DHBNWVNPFxctPkJM+YNRSWJywclQ+QrKPcVpOc+lzF3CUHfT1KWr9PBut71NvNT9RXp8KkQuiCJYwmtBkemWVai2L4HSQ2j8kJjR4+36Kl1kXjoEAACAASURBVN+SsTjc5I+OGA1q2KrPMrQP0TM+KpUDKh2RueQ0mcYd0qcpXJEejcEEvZ6f00KP7+T/FQ5doFTLocot6iZU+wK2jE7+dhnnnJ3wzCqymkKiTqdlQbUGCPke46EXk4hVG9/8ztcRPEM8kwWsepBs7YjpySQ/k3yAzdv7GLYD9ip1kjYPGA4uPvafcPfmh0j9Lr6+yW4+y+TTD1HLn3BQv4tz4pBk+yKn7GOaAiO9THjai9ysYXqjLHz+KfKVbY6P7uEdm+PO8RssP2nBNgxR2VXIFY7xBiOU83kU2UXXoaPavezlyzy0GuNkb0DqqItlrsWIIUpHwBMTGUoK8xemuP3hLRKBOfIbOWRvA7vVxOuTMKpuBobC8cYmqcN9/LoP99+KIIgChmlimiNM6aNts/lSnuXpOTqdNsFQgD/55h9x8dIFzjy8yPvv3cU3kaBSPEDXNYaaztSMm3JHY2XxEjvVEzyuPkF5mr3rNzg6ymFYRiQedXM8vMbyxLOYXZ0DcR37YAprZIj8YJeRUGN+7lGagzJGfZZKIYWmfzIZ61NRQvxf/8Wv/erly8+AHVrDEYbTzmR0jnjcS6qWp3dax+dxo0uHtDt9nJN1OnoNmxEgu13AFEbYRJ222ULxW5BsOgG/HZuzxvFWF00ekescYNrKRAIBSvkqPt2K5I3jsPoYaTqRlQWaRz16m1Y8sQDGqIrD2afaKTIcDPH5Fjlz/hJnli9RUXK4o7so/iJ1cZPBaMTE+INk+1mW/Odw2qFlvgamj7DgQLL16QZ8/Mx/+iK5o33koUSz0yTsWKadh+DYgMCcDX/AzsFOjdZpAbEpojfa1K0FViYepzPU6VmKhD2zZLYNdq/ewx+IYXMpdIcj2sUa8alJ7IEQh7t3WRpPcnBwn6P9MmceWia3W6Z6WmVsQWHrdpbp5RUObuWYm1jjYOuYVrpF8NwIZ1xjZnqeSrOIx2VgajYSySifefRpxqPjnOaP6Q27+FxuRBE2DjeRBYn9/T2WpmZIdwoUuxUMax2vzY83oROYnMARtCBaTGILk5zUM7g9CtOeCURJRY2EmBibwjfmJB7VkCx1hm24dfUOPslCwO/BN6EjWwVa3TrtrMl7r37A+q27DIo5mkad9kmbf/JLn+KhCbvioy/sIVoCtMobJGJjpNNv44ocolgExJCXgzsHdKUa07Nn0UYyghCgYB5y7gmVaecZ9pp5MvUDTjaarMTX2E+d8MEr2zw4G2JpQaDlENi5U+Xu9QZm002v3cEdnuDCmQdIVeps3niFfr6BJWTBGUuxf21Es2ih0c+iz+pMTBWQhEnEwQIz1kVul+6B0KJ5qjIpTfO9l7/O5BccdEdH2McOaLWraO19DvNtnvtJGclmR5Pu89mfXuXWepua/S5+t5u6UqBa9FI+qHFueRJz2MLrOEu6lMFlq+Iz7Hy4e5+xpINh3U7HEyUUsdGdDLLy8GXSe3doperEZty4Ql5sDh2H08HmVoqpRxPky00Ey4jJGS9SuIlh9fLU6kVkm4vVR85QLVfxTEWZmXqE3YM3SE6WOCzs43fJ1KQBa4sTDHQ7hqnx/odvkpyeYymeRDJMJBHGnR62761zurtFsF1l7/gUi8Ok0i9h7eu0hjpum4o73KdTl+m3TLonh9wvFXjiic9jnvYol5oIZp7DxjZR94Bm1Ytl4Ad9SKWWJnM84tR2RMQ2jt2SYOaCi7f2buJzhfEocbwhOxnj5BP19alw8l/79V/7VXnchtsfJFs/wKX2ae33iS8YdMsq9uEymZ1jhgWV6n4afwT6zRAxPcEo3OeDrR9QOHYSD48h6RPsZU9QHV5Cioes9R7rV5tsfNChdF/ArHnRbAKCz0oybhKZdFMr3yO/ncO0gqUiUli3kdsegE2k27JQKJSpdjOkihusH7xFsX2XgDbPjW8ZXJr7Iharjm5VGHkypCsZnlxaA4tBKDRBKGZDsN2n2avQZJdhfYpeDibdYyApuIKTmHjoVepYdSueQIQjoYTV70LDhj0gMxlwcbrTwlAzqCM3o8ou7rjC0vnPcHv7OqZN4vHLl6nbHPS1DCe3MrTbGm21xURihs6gy+2rtwjMD9EH4+TSVRYnZ1F9PpR+g6EkoThkQpFxqo0S9ZM+xshJr2FQbGkYSgNDt+HoO/BHIkR8Xgz9o6X9EjIf3rrCQW6He9UNGjaJpeACjW6doDmLwxVgcSFOs93GF0rw2cefI+hVyWZ1ss07xJwJLKqFzez72BFo9p30Kz7KuRaR4DiGYOBzBGnU/EyMB8GQsVREQqEwZ85OMR4NcFI8pnba5Fd+6Zc/vU5uihKhUAJducELjzxF3yJTHN3CIa7QKt9nOhan84QXp+Gh35eIrx4TNMPMh77A9b2rTEVUNsobbGxt0qrEcAYlVibPULR7adw5IR4VGH9Y4Mq2Ah2BTreMPBwxHewyGRqy/aGbTtlCcGKEW/Zw6dHnaGNl/+BdtH6TiZmLVAs5ir1Tuu0+MwsamU6doD/Crc1rNAcVdLNPdMxCo6mzfdgjPzhCMfwM5QB++WuEbBlyrQ7y0ODJS7PcLm8wrO3Rq/Xodl3EHlhi98O7xGwRWtkcvkAYeySK5kqz1+hjTxrkqjX2Xr/GxEKPQDzJ9uE6NkHAN73E0eEep4d5kpeTxJ/SWYnNM9BU0ie7dOpD7G4P6DpOxclDDz3MB6+/RvDMPJZRF8nsMGq38PgM0idVRHsciyEjWQaIfRutZpFRz2S7ehdLyMuU04lktWPKVsz2gGLmkEKpTMWqY7Na2WruMpZMsH8jy/7pJhtekF1juMJ2/uC3fhOHz8FSUuSwrXJ/+xTRhPj0JUrdEqsXLPR6dZrpHjtXa2DYefHJCFmxTqFdR2lA07ShuAIYQQ3ZVIg0PGyNPuXjb1ZBxK26qXYFsuZdpNYy118/ZuLxXeLjKpv5V+j2DFwzBRITViRzDWsrwWnvhG7vhF7PgtM+wcHhfcTWgLmzF1nfeJPzD0wzUkWik20Ojip47dOc3O8zlxzjyReDbB5v8ee/s8n42jwTj2rIDSfVrJ29/V3+7j/6BX5w+DJ6yYNPCTNymMjCIk7RpLfdIte6x4tffoFMrkE/12dsXuVW/XWmvS4K6RFW7zyRhJ1mJ0Vmaxd3IkHxYITibVCT1ukb19k8NAj6PUy4Q/RrabyL42yv7zEfeoiD0h5Wd42BaBD3ROkNJIRGAVOvk0s5sATSiOER8w9OMjBSDLQeEwteBENCMGuIhkY118MVd9NrHmJRZBy+jzAnbtXC/NIiBWNEJt9k2O/SLF9BCYp45BjiwMQ/10BpDxjWXbTbQdrNHqe5Q6q9HrZMhaW1BWRR4t7NdQ5TO5RFnXZVZuDW8Kkuto53eeKnn0G97kA2HRSrBcqZMpGki261Ts3uQujKdPQK4WQDTakRVZxU+7v0GhYcgXmWnvDTKVVRx2YIiz2G9HEmBtTaRRQF8p0spmMG/1T4o/s0nxCfCpGrdgf3bl0jlSlgF00urI7x0NMrmMEtrEaQvvOIy8vPodpWSOVe5otP/9d0uyJXTn4dSQtwduExOkUJu6FS7ZaQ0MjT4HvvfYjb7WbnNhyv23EKfSwOha7Nwa09OCiryB4Vixhl/80e5x6a5fzfOkfMGeG3vvHPMK11Ws0+g9IP8C6F8Pgy1AZRQgsS06HHKbcLnF2YpiDf4+reDbRQl/AZEb3opO9OURhmEQQrCXeCo16aYdfA6vTQ1SP8xMSv0Ov9Ph49xmm5R99+QqPf5Ss/+9/g93jwHZm0NZFKO4ff5cedOMfA2kVxvotDaSNKToxhk3z/BmeXf5Z2X6ZvanSbe9iyUXoWhWiwieFJkLNt0ypVCciP4bE4kes5ytouuBNoXZHAWIxer8tgZKJoMp7JIyp6F9lukNk08QsuKvEq+UqJbqdCZe+EJzqP0yhWefOdV+mv5VD9K1y++Dyl1BalbJrAWIz1m7exBUR06QS74MM27qCVSzNoDamoMjGPH4dPZ8L/DKZgZ2SpkS8/gM8u4HcrVIYf4BmvscddFFHHl1AIOfqY9xYYmjl6I9jLbjEeOYvFIn2ivj4VItfNET6Hl1YMAq4ejz/9NOu5/4P94xHdbopowopguU7FPEAzdK68/z1CsShdPYvN7mHUX2dt5SdBTTDSYXOjhpmyM3NWQldaBFZ8zCy6KabsNApVQuqQk80CiQf6hB5VkGoysqiyeyvNzt0D3Eknuh1U3cZgYGJT/JTTR+QLbVSHgpy0Ma/+Q0rmFn/80v9J6VDDuagRt1rQNAnBVkER7fTMDmExgT+ss5Kc5sNul1AgRjsw5Fu3/jUz8xeoG0fYNRtr/kepV1K8c+vfU89X+PILX8UZOM+tgw/pjLIshSO4L36R9sILlHZv89IPvoWo9xH9Ni4lZGL+EKcWnWAxR31xisnQJfa3X2ZYv8+gJXHp0Rly2TyWiEaj7sTjspPqt5F8Cvlyk3hkjAuXn6LW0sg2bjFsvo9YiOGUbYhWB4acZ2Jmgt2rVym1a+ydnlBrjBg1mwhdidDqDt1wDmHUJZ/bxxucZe5sj3JTZ5QP4QpZqHePiK76CShTOJRJdOEQpdNj1NeZX10if3ofxA43t7+DdGJB25NwdwXOPNtB8C4hdzvkq32eufyTHJdeZyNvkPQ/yPbJTUbCJ99D/FQMMiemkuZ/8Wu/iDGwslf9BtV6m9xdgSeeeY7Xr73B1JSJYu1TKzVIBtx4+g+iW60cy9dwq4sMpRFmWmHjVoaLTywysyDRMe9QOQyR7RQZjXx8+bG/S759zFH1d9GlPs5ulH5PQ7dNc2H5GQbFBse5FG3pDofbLaRaEntA5HijRNfRpFbtYKkbBGcFRg6RheVZBEeFsFujdneW2rDC0088zG7rXYLDae7VNlAtNpTmNNGFPkLLSatkYT4eo+hqoEk3GI4kCpUO8YBKeS+ERzMRYzHy2m0eDXyZ925u4w44MZx1vnT55xFsFl76429xkt7lMJvGE7JjCBq9joO1MQ+xhIdSzcBqt9LJt7CGXIzkNJljE61lcv6xeSI2J5VMD83Xwi5FON5J4fC7OW2mGOR1+iZ41RF1MY1DDBJJPILdkqHUuY+1luBwo8DspVXqjRpxr8bdq2W0oY4zLNJtgzYy0dUeM7MhrKE6oZhCN7WEzWxwfOeI2EqSr3z576HKXn5w4+voxj6a7oSGjaGRpN5M08xU0Sx2Ou0WgUgCudniocvnODktIEXPcbzxKgOHQTwq0y+rPHRujX/1L36Tbrn7IweZPxUij4xFzS/9/Rcp7uzhPyOQq7boZEckx1bYTb1Nf2CwPGnDlTBoCDkq67PUO1WWp1dZGH+Sg9YWQlGg1Wug2y2EHB4cUY308RvkBuD3z3LRP0EwscZ2aYvjg7+kcjTggS9Bo+rCKqziDH6IMFygtufF5ZxgcfYhNu7d5JXX/gqHM0L6oIRD7hNbHNFK+whM9tBtbfr5HpJiRwlpPDh/jmF7jkxhG5fTg9WjI1W9GH0Tb3SM1anzvHz19+jW3Qie6wQCJlm9S9ARZ1xaoaJVsNucNFojbJUQrVYDLRSjWnwXx2CBpYUYbWpcfesOybEQgiRzXK1w9oGzTKJSb65ztWQyi4Lf9LLV3ufi7Czv1bdpn4548IEpqvk89caIM2eXaTcVrq/fRE32ya2XUOxhlh+6wMYbVxGdI1yxERZHDJvThtI22XzjiOjZaSzKAP+YH0Fu4XdP4DS96A2T23ffoVk3kEN9rBaZ8FQXf8RHT2hi9AwC9nnu3bgLLYFaUeL5v/0zhGesvHPzB8yPYjhc59ipbbHosyMHp9jJtTEFDzPuIINulpZFo9eBW7svkfQmceiTVHt5tG6Tg2s5aoXqjxT5p6KE+Ov/8p/86vQXjti6Y3CwmWJmPEBycZLWsI2jN04jn6OYbtHQXRxuegi6YiTiC1TaVVSvBwtDVicfo9uvIyknWOUe3cC77OwEyd7u4QtHefvde9zP7yFIEkOLzpm1M4SER6jV75MueFD0COWMSL5tIV8sUE9V2d05IZIYx6+GyB0fceazfQSXHb1mo9dqki9ZcMtWvGe69K0amd0WPtnK0uRlKpkmB/kDZGuMSr9IqnqPq1vX0W1DvvTsT2PpGmRPNCxWlXqzQnPgZ6Xkom7k6Gk2JpJn8E426dNC79Q498BZru3f5CRzyOc/+1P0vQ06HRGfzUc4GGIiYCMla6jeU4rDLMNwBmtYID5cxYpOsVsg5vFSM6oMnRrNZob8cANDV7A7rYQc00Tn4shkaPYVHEIYravTSrUZGjX0iki706dttBGHBicbh9jsVkZ6E9OQyPVO8SdE5pfXkMwWY1OTNIpVTEVDrItsXmmSiF9gbfw57AED61qO9P59jg817L4uPec4uWaDzSubSBEHydEpo8l1mtk+on+AosyTbvWQhRKCIJEMTJLK5knMe+jrZXIbVf77X/nR98k/FU4emLSaX/gfZikdO2jVu5yJjGOzJ7l1/zovPPEC19a3MHoiHXsDQY8i2GuE3U4QLQimStlYZ8w8Q8u+gcth8Jn5f8B3N/4t6WyC1NXX8E/HGPcvEp1cRZAy+B2TXN3+E/IbGoEZEatVYGixQbuJ3xEmEF2mORxiMyQ66g9xWIZ0co/RFw8x+nWWzwq0WgY//H0TyelH1ht4x3w00xnUcY24/xwxNUx2VMBm6ZDr7+H32zjcU5hZGMfS2cV0mEQ5y/XbLfzKEE30YTYlnk8q5JM6d/crOIwQiblJmrVNRMeI8ijDSaWEe2hD6k+xtnwOoSsTVgTSwltkxVMGuoRbi2FXE7i6IuXNLK0ShGemcLkqdBwy6VyVpnREpz1gJj5Gqtnm3OIjtIZZpt2w/raN490igteO19mj0qkT0KJkhxW8cRlp5MLo6Rhym9J2lXZ9RHTaihKwUMv3UXWVnrdHuw1f/Jkv897336Rp1ImseIgnkphyg750gn2k0u53aPc1HEQZVx/j/sHryLERo+qIpalFcvkOkuEinohy2iwQmbJj5EUsIT+DzCGZ0QmTygXe/JP3KGTLP9LJPxUHTws2FDw898gLpE5SWKplrL0Gzz3+ALsHmzQGx/jtIS7OzJCuehkpQ+qbJeS4i45whGJaiEQytDIO9rstVPvv4JBKzLjm8V96DtE0uTw3x83cOqN0m0p0j14hijFM0W3XcUuziGERa8AgMnYfQ4fJ4ON0hRS1WhXZZpKYyeBy2Ek3BxTqA4yeg/NPhxA7FkT3JDaHnZEyw2buXUTRjbp0lv7eG3Q7A1TZTzk3QrJ0mA9O0dSsFFrHHA/uIIYFVJ9C+UBlae0Mf7r+NsvWKO6An8lpk662h9XWxW6GsY8sZIoF6oyYVPP4PUH8iSjF8hbvv1pGiQTwKKuUyi2caoeSp0M/lCdUnWcsMc3hXp5hTCMg2dg+ELH1vOSqfSbtS6StOSbc0+xutGgfHCI5+/ijLiqtJkrETVRwInsaBDwy6XsO8qd5fEthRlobQRnQ6tgZDa145uMoukg31UZ2N7mWuoM16GLMYzITT3Lt+n28gRClio/ZuSCC5qZXzSNLNhznrSQnJ2iRoyOWuXb7Dj/1uZ+ndyKyvnEN95SJVBXY368iNzN4dSut9AjHGR3R8imvk2u6QMj6OJIRINPYQm+WCXnszBhnWR6rMzaxgDEyoZHivMdDua2jL7g51Su4XVYKjRo3Kz2crSmiHpV+WfoImDUaEHJ/tNnpyu0demqZ1TMPsnu6ibcl4LJGEcpxxi658QXHaWg18nUbEXWWcimPbhthCHZ6HTdPPvQ/sn73Zeydq5SNLMWSTuNkxItf+Dn2ir+P1/UIY7OPYuzeoV3OIplAyYbgNMjsOjh/NsFecZ/r79wnFoxh9OPMJ+2YiSox9/PsONY5OTzkaz/3j7FYerx29Xu0tnucksK02bGqe3gs80gFNzMzXnIlGcUZ5CDzBscHR8TGJvA7FxgUW5SNGvunTXwhgQtTDzPx1UvoARmbw4NQD9IwT/jPn/s8N1/7gJJTwliw42o7MIJTXFqJcCX056h1LxUzxcihEfWGcPjmabYhdXxAviOiTPnpV5pMrk3TaeaRDZnBaITcbSF4RRIPP8D+O6/T3i0TDYbxhMMcHB3jUEQG2oiQHEBshAkF7PTVNBxLSJ0yp7dTuJYEtJGFpemHye/XKKd2Cfh9YLNT2zKottJ8Zn6NdKlKryLy7vWbdHqfLPJPRU7+T3/jf/pVZ7JBJBBD1G2MeSK0qy1C8TNkzSwntW0a/QbV0Q7V0QhdhJ4RodbUGNQ1kqpMzJHEm1zB4QsSij7FyDLCHZhFd+i0ehlkj0y2vk+/3UDSVZYunMWwm5Rp0BZH1M0a497HwZjH7GQ46K8zaG4il2KY7SRiJ8Ubh29TzQ5Rem7GPM8gWp10hgW8/jFkMYCs7bGx20EYBKmV7rI2d4a9oxM6TRXXgog1puL2uJkNrXB9awPZqeAbm6HXcVOVDS4sXqC4e8zY9DKnpbuUh228wgQeh0hAmeYgtU5iOcykbR5EO8pQ552bP0Sy2PAEzpK+ew850qTZ1NFNC91uk8x2ge3NXfZ3b2ILPELu6ltYFC/VoypHhSJtV4NSpY2QH3Dv9nUyx3vkGxWmotMIPSeCXyWirlDPl8mldlCjSRILc5jdNuGpOK3SKaYs4Jnx0K20iU+6CagylXIKtyFQzHaJnR1DsYfodQwquzVEq4RsteFwWHG7w3gcHoyQiTRSKVqyTEQXSZfzyIJOk336psbIVaF1IvDsU89ydHpAfNZOYZQnGltD9dqo3a/x3/7SL/14OfnHzKAbQMY0zRcEQZjiIyhWgI8Ynz9nmuZQEAQb8AfAg3xEffuqaZrHf92/XWGH+fmfex7RVyCTGzHjtRMVFF67to9zzoY/Os4Dk9OkS3W61WPs5UnK9gzBnp9t4RirKGO4avSGVuYCF6ke9EnOqgybfjLSMdX9NC1bEVFuM70wQjHOM5Z4kFJnn9OTLQYNjaTfR2/g4aef+gX+8K1/RlfbZ3hkR5AUzFGA8WCCJy79JHdrr5Fqv4M6CHNx5Rdo1Efs5/6SQMCBIqvoA4NZ/0WOs3c4NZoI3RRO4SJF4y7lokHcUNkp5QirMRSrnfG5EEFXAot7nNlgGGWkcefwJofDNzA1D1Y9TsgRxOiLpCq76EMX1q5OeBxa7RG5owxdwcFTL1xg2DTIlnJ00k1szgC13jHuuI9u04U7GuJSw4fLIXGUPuZwoDFULaTzezj6VsROl25Exh1QaQ/qWG0dFsaXOcymiAfGOTy+ilOdZW3qErunm9i9MktLl0gPvkG5MKBVFnEbMSaCF0m1tyjdL9DfqNML2Blbc2LqCdZfv0Zo1knikSRPjT/PbneLqZCHV793h5XHPkc4FOP+vbdwuwYMs2XuHm8ztbyMIPbpDg8pXPPxwJllamKTcESimDuiY4Y5Y5vgD775TZqV3o+dk/9D4D7g/vj9n/IfCTtuFxU2jm+i5GUMzcHdZp5TLYQrHmB745iJrkrpdBtXuINXnufAcoMnJh9CG46xkyogWeyM+m2SljPcff8KaytPkdcLzET8FCoj7GqYaitNXFqGrJuKfoqhv8ThYZpK0UTv2XCuuOnWdN66+tuYPZ3qdgDF30YcqNijNu6sbzPmieIIuShthujKQ/blHLqlQbc1iVWrIYfXsfYvkog/ytL8k3z7e3+IzbXK9NQszf7jvHv6EotLUZYenKFm3yLJixTzRQQ1S6b9HpmCgFR0cb9+SPdQxTEu4E/uoXbG8cRV2jsQCOgYtg6dWgiXpYv9oSQN2yGn27fJ5Qo0hn2smkFiLEI8mMAdv0ipfgdbw2T/aB9Lp08tpiIEbVgsdlYCD3P//TcRJYGYKHOcqaGoXZZmHmFj6zqORICrd98noIp4fC6mFh7g3Pwa+XSWo+3blD0FhI6d1qDLsFNn7+0awVUb555aYMM8QApUsPmmkexdEExK6Qall0ukvdv4Q34O5zQWLo3zsAt2OydEQioW9yRG1eDFZ75Cq9+mWeijzLk4f7lLtf0eMz6J/b9y4ZhyMdZ2E40oiNIndzz/Rk4uCEKSj9Di/wvwj4CfBkpA9GPq8sPAr5qm+ZwgCD/8+PnKxxzPPBD66zCHnphqPvufjdHpBDCGHSRhiNsSoSw12XrvAGfIjTmQmVo1YDjCUU8wp0bYlcpkOuAQZDLpKv1Mk5/4qWcJLy/T16Fe+wCvx4e7VyNVs9J3t9i6fgdJtuDpOdADLQ7Xe0zPruCMyJRyGbR+k+nVBYr7Bcq9Mg6nRMjzEIXTU4aVNrKmYZtLMqpkOf/08wSiIcyexPH+Jp1SETnaIGFf4DMXH0V1rzAy4Nr2FqfNTRRdRbcUmAlNctB7lW6jgaYJuN1lmh0BMoswrHDrRo5RW+LcZ6cITVWoHyQYDkVCMREGJn2nRFvYxzWQGVllqu0CkbCFw4M6UqvP6V0L8ZUo0fkkZ+e/wvr9N7DZJhh1h7ReeY98q83SxYvozjaqkuDozn2EBT+94xKSpUN4NoGkhak1ThAkG5WNE6SxHqJgEo2GWV08R7tXoaXfI1Nt0Sx3mfY8Qdy5xCvvvMbCkz7ssspSZI1bL9/AF4fY8gpX7nzI0cYeydVZ1IEPOWJjMrlK/uQKB3d3KDuqXHjw8yx4Z8kfbBH2BJB8TmRKnNoaiI4dmsNDTFGntW+n0QuxNO6H3hzf/N++Q7P+o5tBf1Mn/w3gvwP+75HoAD8mdvw/DNFi0lAy2GwKRuUcopSn1m3T1jQmzwboDax4pCSdQR5rO8KgV+S17T30hBfTMaB+nOfSo2c5clU5PLxNQz7ggbOfJ+L/Esel77FTv8O5xDInGQG/W0WNVRgaJlLLyuLSA4Sm1zg8+SbtNPRbArV4HcM2hde+giE2KTcaDGsmXqeOXZ0nPj2FbQY/ZgAAIABJREFUJXaGfHUPjzTklVevYps2WF17CrulxdTYRSTZx9s3voupRNgu5xkLzNJuZ2jVdrlWuM25ha9yrfPv8NDD0KYRCWN1G/iiMzy3pLB35xbyxJBmrUOuVad+lKfcs6GJLTwTFjS9TWWk0z2WUWUbNzb6dAYKtn4AYdhnYjxGtd5i8+BfMtRDhLx7DJQ1BpdmGe3c4r2Nt3nq8jNUT/NUqlVi+EguJKi7+tTK20TCFi7FL9OSquxYU3TcFvRBF9GnsdV4H63lZS7iIy1X6aQsnBaLmEmVZx9f5Z29K4QWB7xzPOL8IzEyxxusV19jfH6Nz3zmJ7i/voU0tJI1FHZT17CZTiLzYZSuA1F1YldDNDs6AYeC6I2y33wfrenis3O/wiubv4fFkqcsphmKZcqNJJ9ZW+HlwFufKN6/Caz2BaBomuZNQRCe+n/6/m8agiD8IvCLAIpXJOl7AVWf4LS7jlNzYopOuqYBZQu4KnQdBZY8a9xOZ1lcWKZs7tFt5UnE4ujOAZ2+hhm24pDrOA0vb7/yBiG7nb7/AMNa4t2dtxgeW7CPhzCUcaq1Kg9OzPDA/C/yV9/+M3r3dNq9GrNz53A5vKzMnSNXqNOuDmkJJpHVR8jvvEdXO6X1bp7Y9AwNpcMf/+l38U/MY/YUrr78JvZgFTsOBM80dtVkvZRBMtqcZu6QdMepEWb3+CrI32DvzQpuq52hmOHii2eQnD36eOlKTVwLEzQypzjECC61TuiSjdJpiGbKJKlHkHwBdnaPqZ3UMSZ8jNo9UExWLz7OhKhwv3sVn32VsxOr3D34S9K9XfTjCvsfaGAR6Tf7vPbtHyAho6oK9YMC6vkxxhNrROef4cOb30WOj7i2/hYx/xRzvnHsToVGu8uov8lx95SBdo4xh4D7SQunb/S4+s47aIJBfE2lfDLA2q1y1yIT8gxYcayycbNO7eA6X/rc3+ONq3+GrF8hUz6iteli0O/ie1Ak0KnQEA+ZPbPIwXtvoVS2qUlW5HidD9+/SaU+yd4HO1QrTgKXeijOO3z77i7Vbun/u8iBR4HPC4LwU4DCRzn5/86PiR03TfN3gN8BiIz5zey7KUpaFsXRwTTtJEIBWrqIZyHCSSXHhFXBKgYxh/doDoesJSfZuHuIMlpgkH8LbXVIMpwll1UIOVewte+RavbZfTnFyuUAo5ZMqSYwKYfQml2W5idIF0Sq6Zd4/5VXGAgd1JBEsbHP1y7+MoVCmccfeRJRe4zcsM168YcI4TRGVWZ2zoUtuEGt6qO6FUAYtegcNTDLI1TrBFc/WKdWepvZCRctQ0BOCjhsA07KJawtD2eWxtHMJk6Xh6BritmFs2S3r5H2rKNK04gDF4rLg1eR0ZshHJ4Y+qCJ12rDMm6lXbci2zosLiyRdRVJ7aR5/u/8DLfWb3JysEdxWGDxKQuZ2nu89d4+x4eHjD/qQYpqJF/oYzUkqu8kmZs5jxWB8LSbI71GLDGNVBd449Xvce6pS7x849sY9RBWh5ehxULCeZZ7t/6ArrJH8UhAVDdxlMcRw7NML49IF08ZmiNGkpPafh9VOWD+wgXS61cRPQP6Whqj5uLN136XQqdJsZVi0EjiCIYxazdpHsWomtf5S+MlZBNc0Rirs5/Fa1oplb/L0TDPzGyYemqOeMRN+mSDYgoCST9Cv/XJhvr/puP5sZP/44+rK38GfOs/OHjeNU3z3wiC8F8Cq6Zp/v2PD54vmqb5lb/uv16/1/TFncz+pA2zF2Alcplus8yoWyZV7+Kwykwmx6hrArd/+CZKWCDiiXDYNLAOLNg8Th5Ym+Pm7rt4PEk6p00OUnuodhk1EWE+PkezfJvaSCF1N4Pslpl+TKWRDjE3U6a8p1OuKbTdI+Shxsy5GHp+yIWFhxgfW6XGLi+d/hFepY+kC5zxjVHSuwyEJWq5AKf3DpmKLiJZJLaPv4vdMYbbGWbQr6L3raydnSA8Nk6+tU6rZOCQZPJNL5qZJ6Z2saptPK4BW3cksr0hkfEp5sYSSHWZ2el57u9vki9vYNXibKzvMDkZZunCFK/f+C5Szc34/GVGwwyPrL3Iv//df4dztQ9mH0GQ6Ddk6tUSYaePcq6PGu7TTNmYSczw5Be/jCeg0B1VOUzvMJ28yA+/8Q16TYlAEjLaPvHJOVp7HUZ1kARIFU+Y+YxEbz9Cs13g/EPPollkpsdnaHZ63D/6OgPdREGilNeI+5dxF4csPf0wpycbVHNdeu0m2WqZ4WjAuZWnSGfvUO938SwnsG83GF+aQh/Zsdh8jD00zr3UNo78EdmajbDkp6vU+InHP8e9q7uUBhVub96juHXKoPOjqys/jsin+aiE6Ocj7PjfMU1zIAiCAvwhH1Gcq8DXPqY3f2K4A27zia/+LA39Q4KqSiyxxL3dMn6vF5scJmYzKOeOCC2cp7JxG8lo4lQ9rF/bZXpmkpsnZRKLcZrZIoX6kEY7i81U8U458Ah2PB6Z2++doMQNOu0+kQkPiQmR+r5EqVUjEDKIx0Ps1YqokpvBQMHSa1ArqjQydRx+kcjyFFa7jDVwjN/jBl2lXHAzqDfodVokpHEsqof9k7uYehv33DSqY4CpDwl4Etj8Jdq9XaoFEIwVHn3wDLc3D3DYWojmMXpMY971HIL1HIpdJLNzQLmdR4q5UQw7ct/O5r0/Jz6rMh/8Am9c/zZ2xcAZO8/ixHm2rrzG/JkY+/eq2EJWyu0alnYdwfBx//1N6u06c0sxAmt2rCM3bosX52SBUTNAadjA5x9jrhtir1Amn6vjjkbxBOzUWg3KWwWO7qwjq3Ykh5XJ5zUc2TlyhWMWHr9M2Oelar5G58TBxkaBsWkbTilCoyPQb5rMaU7aLoPAg02CvUsUCzfY3KzS7Os4Q1baihddN4glAijDFsXSAfMXLrKS/CqaeZ+N0iaRwZD86wWatSFDU8cZ8zOx9BD2RIR89pC3vv5t6vXmj9/WN03zLeCtj5//o2HHVdXNz3/ta2y9H0QIfMhp28Pf/tzDTExcpNPX+MbXfxv30MalpVW23WHUoYVav8mkaxxZLLLgkdk9OcBomwS8bryyl5W5Bd69fZPknBXJ7sAZduC3CwwjYVrtAuWiBeu4BemODbseJZtucrrexBaVEHpFtI6VQNhB5OIarWKBZGCGwmmRnQ8klPE6qjeHiITkGxGMW2gVJlA1qHVb+MehVd9j1PEzHopzmmsxvJtHcPqYmozQGupsZIssLJ3hZPs+ibkEqieJ1q0yGevhdz2A2+rnlfdfZtITR7F5kS12jB0rR0ct7MY+y2fOYdi20MQtai0vNqnDhy99gOEbERPmSXoTbJXvIVHCvyBhbrsxDAudAwuJeJDoUorysIArkcZyrNJq+3in9D5PPPIPeFj08PbN18k0rhAOTuN7UsMqRknd76DYBXz+KJqti9slsffOBxjnz2JWVti5cY9QJIGj72TaN8/qpYdpVTt0zQPu994h36hwZ/Mv8Ko6pteHUPNRzpbwR52El9yElC753gTlvQrG9WOW42nMRp/cqYbml6kqPdKjFqrmpZtvsnvwHYbtIZpHodnufqK+PhVt/Va3zvHW9+mKV7D0zjIfXGQ+vMSo22BQqhLSNcpmm9/657/BY88/S0uV8YZstAWR3Q9btPQ6qY0Sk4/H8CdM+psOjskRPbeIOhXn9mvXsYVVrG6BR59/glf+/D0ks4Gta2DRZbayp4yyA0TRyej/Yu49nyzLz/u+zzn3nnNzzp3uvR2me3q6J6ednZnNi10QicgmBKtIkSYpR9likYQlFyyZFCXKVElWiWQVLFIwCMpEWALEAlhs3smpp7unc+6+Oedwzj33XL9wld+Y8BuXXfP8DZ/61ff7/J7n+eZVtKoFW3SA2lOZH4piPXOScu6Qc8/NgB0K+kMMmpeId4zd/B06RYkvvHqFlZ17zAVlWrkuWjWEWRpw+OSAyIUTbCzs4j1lxGw/oF2apV7YpeUQOX5uk+WDLk5tHYNgJf1gm/bWTS5ffInJYRcWscawf46VJ28xPWujZ+lgS0rU2nky2gbZdQtyN0+3o9OqKxgPbGSt20ye0uhmTdiDJfpmiL1oR6i58djjaKUOt7/RYxBxIA/VGI/Psbuq0CwaeavwHxCKVtzHQzjMZyg37mF3hJk6HsBaMjL60imM9irrOws0OzpG2c7ajR2ic3E6gx5Od4fEqoppZI9SYRurf4j8Vpl0x4jDG0SoFkilB3RlkU61yCe/9EkqxQM6pLl7v8fclQnGx6cZNFu0du+hWOyMutp02l50BVSDEbHXwSB5kZwCgsuE3Walk/y/2b7/q54KyDVV49/+6f+OpOpcftWEYbnNwoMHtHoa2bVVilqV9lGXdqfHk61dvDMSXvMQLUcee2uCkZgP41U7pXyKjKFJbMJLwxhGdh7hNPcZPlZg536X6sCGb3KD6fMi+sCEX73E0tIPkHp2mpUazjEn3cMWklVn4oQVJaexducdUskSesfALZOAzeVAdrhQFJWOu0Z46hUsITdL29us3H2EZUjh4uRZtK6fg8QeM5efYywsse5YoJ5vcOdAIzq3TiE9IOQxMhQ9TSyc4zC/TMRyjnBgFj3c4aB6m0cPHtHSjJyNpxkfmeN7339IbHaKV7/4cbLpDAt/fpNmTcTQ7HDyyiw7Cxm8bh/FSgskEy73KMVaE7pdbEN9QsMNJmKXyB6mqFkHZA928TYmcLvHmI12qQRkHj7cxym3ifXdzATnqOgBHu3+GFv0GRqzByQb9+j3mzQ1ldCUGeQ64eQMZquJ4LEhRFlh8twpXn/5CrvZIhtbP8NyrcVnQ9dJ5Q28/1c/oltRkAMWpuZtdHM5cvkinWYJtWQlu7RHJO6n1BBI1J3ETV2a1VWcpkt0p53E50wMyj1yD3N4hkexy27SxRyC+P/yM+j/65Ls0sAmu/COeBg4mvTNdvqVAWaXAOk2fYeNlqYiqX2slg4Xrr/KbnUNtenGJimMTAxoNGUM/hzNeplrF76KYeCFgZc33/xzepk0uSMIeWOceX2c3eQKiEESN/fI1ssYqn2MDpnAaS+x0Hmq1Zvgr9PIOejsGsgXcnh8Ec69dBWL38PNb/+QMy+fIjIxy8DkoNWsYHLBxtG/p97tYGyOIBdkIjGZwopMqZDDaLeiGUsg2LEaVGrdJp2+hrXj4OSZGRTrDooq02z0yR9WEEUjfVXGF3fRqTQJREao1vMIUoMXnvscN394j15fwCiY+PgXP029vcxB6W3Udpe+4QyWwSiCYCG/tUM+t8Jzn43iokzfPEWfCaq1HQ7STTplnfNnx1n44C6jZ17EY3Ly8O4i2bUVTD4b188/S3TWxk/vvUNNT2I3DxPw2Wjs1xmbcFKt2minHGhqCbfDQX90GGtfYmJiCEFu4h8dY7/4r+nrTfyGr1I/0KlqWdYSdzFLCu5MEGnMSsh2gquvfY6e0Oetn30Pye4lGopQzNwnt3eTcs2HK2Bkc+uQiZExAtY4b793l9DkDNFYhBvfe5NGqfb0bgaZXNJg9EQMf8jO5MRJ6AikC0e4ogkCUQGfP8Df/GkDt9vB/Md9iPohuVUnofgrjEWHWFr7Jn4xxEL1Nq/MfIpcT+Ha1Gf4wbe+w6233sNrtXHs0ggF0Uli7TGhmMr+ika9pmA0WnBroHldVCtZ3A4/Q8ds1FpV6hnoKT2GjoXROn3Su2nmXxwnMjOO33eKXqOG0eLB6jDw0Xvf4HAphUvzEzk1TLWlcu6FBha3mcN7LtSajcPDPWLTfgq7BSh3kRwuynWFhlDEgYM+fUZmnARcNqpak44uYDc58fijGAwu7v3sAxqlIu2axtUXr/HK5z9JpapQTx+ykv4msr+Fpv6fKW1Tkd/E4wizvXObeiXFyIUhdLXB/kIDkz1Cu1Ynl9gnNhtj//E+w6cCXDj9OYqNAw6W7jBoD1i5n0O3uXjttbOYnW4+uvcItzdIaEJi904OT1DFKEQJ+UXEnohDLxM6cw6TxYLUH+HwIMmjm28ij+ZQB22mY18mv7/Dkw+WcIb7SIKOU3FiGjKxfC/DyOQowZEhOiYHrqgDe6uFXlpiab/BWCiOJxxm+e4iJ0JxTENOurYk2YKOZXCc+2/+Da3K357j+VRMIf7B7//zrweHI9AdYIi7yB4ksXo9dK0rqLKG2q5hsw/zpS/8Nxx8UOThyiLPPjfJeOA4Sw/fZ3fngJX7a/jDRiqtKqIOv/Dslxk/NkuFVVLlIpqhjGR20u2nCAXDxEMXSGynCfvcmMx2BINC9MwERztFiokaVreDc6+9iNjrUCzkQRwQn4lTzdbZebyH2G4htgwUj5Ks3b3LwXIGrWfiwldUxs6VkSwddClAYv0QVRswHr/I81deIey2IbnGUawqn3j9s9xfvkdHaaNjw+FzUk40UeUBw6fbGFxl7KMlQsZ5jAwze+4aQ5ERKrUS5XKJh4srdJol7n3wAbJ7nP3VMp2qwKBpR0nqqJkG2tAB5y5ewKjP4+U4NbGK1l/BFvISHp3BN7AyYp9hcjROtfGYsXCM0uABDp+FtimPy24imUkw6h/l9LULnDv7HGqtwNzkWcy6h0x2n63FRwiWOhPHZygK98j3foZW1Wl2D0Dqkc5pWKRjVDN7NFUH/b5CqyYyPT3CyJREOl8Bg4fZC6PsVzJky3kGgM/cQKs6mIu50EUviBJ6a4BzMo5a0zBarBiDMBT2sru0zm//d0/xcSG7w8aJ+RmOnRXQehscBmRM/TCZzKcppXvYIn767T2++xf/C+eeiWKvPIvZPM4b9/49r534DAG3jPi8wlvv3EW1yujUeP+D7/I4eYOkeJ/IlSkURcLXjzP3uXFMtiKNhSpWqxWTLOOdVPnPXv2veGPlHfyfH+XBjz/k2LUZEonHdP1mpmIu6pqXZlti9uw8XiHET29+wMCUw2yTsNkcdGsaE1fjZA8stFItvDE/VsHNxrLOZz/zBTZWHvMffvA92sY+ly+8zDPnP8byvVWsgoBmdWGWjLSTCn2rQCfTpKQ3UBsDDGaVjmkLr1fCbM0imwPMlKPc+2gBq9PIQaHEQDaTfpTGM34Or9vI0dYew6difPIrX+IHf/0nvFf4NvmVOOevTTPhO0uir1GrtxiKWXHJk9Rq27z7/k/ZvZMi+upHyK4Bo844cvoYpUqFs9dPc/Hs8/z0/nfZF5doik0uXH2Zg84N7LEDeoURKAt8+y/fxCW5OffxC7R0K+u7yxQPO2j0MDshfHwUtVyirzlo9iSe7OeZ9qhoXisXbW76Vi+RKTfOwg6j8TSeynF2B0XWUgmi9gyTI89xLv4pzBE3freNN378vxExncVt1+ipP3+e/KmQK/GpscGnfitGTVmh1/aR3orjjKXxe89iM56gmE2y9e4DlEGXq1+usZkrI6xexDfvQKvdIJEwMzQ6Qq2YRydMajnH2MVRlKZMfNzFoGpFjHcwizql5nsUaDFlu0h5IcL2zgHhFwRORIbYXNmkrjUoldoYW3b63hEGWguzKOMZUXEHwpRaHxFQT3Dl7K9z//AeNlMFu+ssazef0Mpv8dLzr6D2euxsZXDN9SkW/bjsNbbv3sXokpmYeI1OoYZTkLj74A59mwaaQq3ZQdbsiKLOcCxA27aDbbiHwymjG+rU8wL5DSMGUWIyOsrGeplqXUFWRNxmN4ETM1x63oAmrdPIX+Ds7OvcefAzdlbfoSdKWNyT5BN7RBwhJq+eoFnuIUttlt/eoFtXsU/ZyK5nqPfq2N1wPHKKU/PPUO3WebT4Jq8+d5VYeJYHpTepqBq0mxQOS3hH3IwGT2E1t9ja26cqlHHg5++9/s/IV+o0MbKVfJdCc5mzE9fYfS/JbneAXTNT6R3S7uaQ7BovmGapXT6Jz+qlWPwB4r4Bz8RF1rces3u0gtdmpSGrnPC6kaUpZGsAZ1DAIhqpdpf5zr9coJCqPr2a3OayDKLXAoimKvF5kcSqE3MziD3WwO+x0yKLno3gnLXSZQtD4QSFnTYT0yPUW2Y2EttEvE6K9RT1Q4GB2iA6G8U/UcRht2GUJ2lWZTJL68ROGek73KiJDm77KHrfTs/dplDbIp0qo3W9lDcS9JodYi9cwS53kEwVhseamA0+3vphk+JqmpdeeAFP2Med1JvMnAwxP/FlstuPEc09Nh+sYbUN45seo2NoUWr/kHKjh9frIz74RZ7cWcBtDmJ2u+l598hmd9EVCyZ1EoOrSrtRoFap0yh2uPz5FzhYOGD/5hFWu4Gx03GapS6FRA5vyEzPZad92CEy4+CVz18n1z5k+dYCBlVi7jkBXWzTVx0UUjGanSU8wihJNY0v4EfCjd7L0zpo0+0OETsxyoQrRFdwYLWvsPhYo3FYxxLfZfuexhdf/U/xHffx1v33aJgW8dgEmoMOYs+I3+YmWy8zUASUgsjrF3+F2ck5uoKZUq2ESe/y9o3/iJDyMfvJF0hU36GvqfQaJRy1UeqlOkPT40TcxxG7m2QOVRxyiLX2Hl25gk+SmImPI3b89CwFko0U4sDA8tEjtJ6J3bdKtArq07vjKUg6Va2I0DShPugyaPfxDxkZmeuTU1dp1ns4pupoRgM0ByjdFB3JysF+CqfHjUmrUS/00HMDIsMWLESw+QzsFfKYuhonQwMuTP8qa3ITs2ai2ejxwRt38Y0VcMVDBA0KlcM2wWN2TCYDnsAYYv+A4MQ2xYyMIGh0tTIWe4cXP/tVDudT5AtLnB+dZ73qZuHNXe71fo/AiJVCvknEPcKx40Hev3mT+bPXaXV99KsZEsk6ib2/wTjwoPkyzAkwdPIy6xv74KgT9C6Qyg/wi9NYDV5kZ5/dO1vMXL1KK6fS63UxGDqEL7e48vqXULoGbn/0Nsefn8ITBZNhirBtirVsmxKrZPYHDMwK3sgMPWULQbdQqAiceha6SpHGjgfZKxK5bKbbLOEwullczGEN1GmFalx/6dM82v8JWJpcDp3CEfJjlnygKwR9LqZdv4SBJkuJH+GznaFYvkkz1yG3YOWD5iLpjQIzUwFEV4vFvSZW/yih4w6E+hoNfRmtK+OsjWEODhiIDWxmDdF8QL/do59Lc9A5xHstxmxY5+7iOjWLxmH5bVZvmbl09hora7fpFU0UALXz8x/rp8J4/vM/+sOvT116hkY7Rcga5PpnRxAlB0dbGqg15L6N2q6G2WWg2urSbLcoZXX2F6rY6bD9KIdFh//+736Jx9s5RuaCdDId0ok0Jo+IR/ZRqTXYKK9SHzQZsXTxTL7CyPEh6kqb4aiLq8/+MuWWhF0KEx8dJ6sGKBVFusZVSodWJFuHsGsOr+ksO+t3MVrrFDMNTs3HWUks0qpqdBo6FsHP6OxJbn73JrV0jWZthwvXvfR1mUE6jnM0h2NoQMgZwW73k+5noe/DExjHIlZQ2hKnJi8wNe5HHnLTaA34/HO/wOlzV/BMjeIPREmmVuhb0jw78zztSh2jq87w6CXqxQoDUaKVy5DcyzF3eopB4yxBexC74Gd2OorJIlLXDnFbJojIMlbH67S6CpLQxYaFZDeF6iogdWO4RBVNK1HPdpHFGMdj8xwd7pFqHlFPlWnW8iw+XOZjxz9PammPjfsl6gUbkRkXm0sbdNoGOgONyOgciuEQj+ksp469xuHOO+yV9unmIxRzFRRNpdbqUKZEKZ2htQGtlkbfb8U6OsbWQZNK2YKs9tAUGasnQPIoi6a40K0uLp98hfVHC/zjr/3jp/ckhTfgHcw/c5qqukunozBwK/TbA+yuEC6TlVqpxMhMnLphh2ZNpFE1IqtOSocNXnnmODOnP0UjeRuz3cJiZ4OR0HOs3F3kIHcDhygTvezncL9N0BVk/dE2589cZCgSptHVaXPA9Oh1fKYogkPgxvI3kI2X6aOht4pk0uu00hq1SoXP/eIMOysqQ8OnWFldYOvxKr/x67/Jn/3om5i6CrWcjqr1kZ02+n0wmQxc+1WFIe8AxSAj1IZZSK4TDQUJGa4zkBoUK3WUjgu/zUti/zHmvozuS5I+6PDix34TS9/ExtoRwRMWip07NIpBhobsRMzjbNx9xGvPXWO92sZoNmKy2jk4OKQ7UBEMdWyhR1T3zGBt4vSrjA4+TU4zkOm+T7PawSpZifo+RrH5N+QHOSyYCIrjpLRFBl0jLuswpXUXTcmH3SPjdD8isaAyMXWFxXsfMTl1gTPxKwjdMm88uYHFm6DfFUjfG4DHzGgwyvzpKMnNNFK4i9/kxWqKU0ovcX87gds8YNhnoW+t0pc9eMZMdB/KKJU6fb+Rw/0+J87PMT4SZiX7BJtDJp1dRu21KZZVwvazeK3D1A9WebKwS/fnDGg9FS/57//Rv/x6x9qkWe1gCY6g1nSUikAl16RWrGNo2rAYHAyHrtFtOFGbMmJVZSjqoW3ycnx8nNGZc9xYWEFwq4j9MPMnRlCyZqzDNVL7GnJfpr4vMjF9jlYyj8dkY2wyTp8aZ2c+wcbWB9z96W0q7OEcyiE27dz/6X0yj9I8+wvPsZdM0Tap7CwWyZZTBL0uSq0WY4ERpL4Lb8iF2eWl2dYwyGbotpi9ehmLX6bRL2DQzrCXDVFMS+QyDnT8FOstUvnbDCywtbDJ2LGzSAYPvXqOne062WSFpllkeCJMtvqEeqVHv+1Hp0o8EkXyFGh0ZR4l3ieTKxEK9skoGwgUqGqrGJBx2s6gdJpsrx+QWNzjxPSLtKtuRMWB03WRmCHF3eV16i0Zg1Wgr/qotDIYbBGmx14jZJkk7BVplfapJs0cO+lj4tgJrGYDrUGeV599jTeffIe2WMEzcp5GUUNpQl+344s5ETsVUokO9XQVIdxi+Z11BppIyGqj3ZNAMKPQpJ4y0Cy3MI0aUGw1CsUuuiZg9gd48HgVoW/k3PwZph1htJJCMWvH7xoll95hcniEw90Ev/u7f3vSxFMB+Z/86Z98/R/9wR8YmHswAAAgAElEQVRz784Duu0ehbUsQlekL/QwNCQufsWJolVotUv4widwBoeJOHtIwQ5qK8bKxjvc//BnRIYt6FKHgZTHq5vIVxTapDHYTZT2a1y5fp3NpSXy6To9owOjexOfMMpAKvLj//gTNh+lMSlGNhdSmExhNNGE2xMgOhIjsZdjcm4WZA1zoECuKRCcb2M1H+Ps+cv88I0f8Su//sv0VTja3cXqk7E4QwhMoBRdFDICvrEYF07EGQpO4RiuY3Ov0WiYqK7XiPrOcWJ2nkK1SqEjUspWufLpTxEMTlEot7BoHtzmYcZdMkpzn46hSU/wEwnH8TnCtAxLWEWRXCtBt5DCrPSo9joY604axRRmo4egZ4S6sMHy0iOS++uMeOw8WlyhXfcRHztNrbZOudZiLv5xXIUZWkWFtbUn7KUTbN455JmXniXVv0er/QS7HKe+28QbGePYcJyjjRaGchcBC0ZLD0E0Y7bmKKfLRHwyWlcnd1hj/soQNpsH39QZurqO4BaoVHvoPR1d9qJUe4yH5yiXdY4WjtBEBVHtU29VOdrZwx8OspdqMvvsNSrlfbqdGqaOhUwuy+/89u8+vZD//h/83tdlf5LNB6vUjmo4rXZcTjfBqIteWSHyTBfBVccq64gZB97AAI/HjuJaw+h+QiRygbJzndaBHZNPp2NJk+4s4o5INPQMssFLX6uit9p0u2mkEQPBeARRLqBmO5iDJZpNEMJxRNlA/kmN4kYas93Cs5+4Sr2eYnT8IlPH4vTcj5H9SXxDXrycotDucGw4zuLt+xyu7vA7/+0/QFf6qBZoq2VE8mTSSRJbB3Q6LUyyA0mpkTh4wvL9HWy1GGcuvkj81BiNSopSo0er1uU3fvMf8mRtG5ehhU0wYzBqJDcWESSZwWBAv6UQjVzH0CwRNgpozTYJwzbNihe1oeKVDfQEEyEpijRySChooS03cNmH6Ipu/KYIvVaeB/e2MDpFdtZ2UBSRcrKFo+sgV28wPHOeZy5eZerYafI724wMxxk0enSKcVp1aFRVfAEZtxzEZ7VQMq4z+cwEPheIIoxMOTCPVZFSLo7FRtgttuhUKlikKsGIA9laoFYb0O+IxM/GeSZ+nPX1TehamfRGqfS65A+z9Ft1ogEXmi6iCC0unbvOzY++g7GgcunMNO7QNKvLi/zuz4H8KemuqHRit7n6y1G0doR6TcfpVth+L8uv/df/BUUyrKS/g8k6hMEtsrpVJxDcpWdp0VMseN0O9JSVhfcXcG2YGbvoJ2Cxk2ykEYe9WExdhsNjJFMlWgUZY0NFj26TTUocF2Ry+yk8MybMZNm40QKrgfljJ7j4mevcSn4TwVIiaMmyk7JR7m9iN9uYdZ+jKFlQHAtIrh5XftnPg79q8z/+ztcYiw0hmRXMYZ30ZorSfo2hcJix+Ai61mJjd4NUagfZZWDywjy5RondtXfxeSvInjkGvTj3H93n0vwZ3r/7BuhgD0ZYe7CP8/PDeCYvkdl9SKK9iVXJsFvsELdOUuuV0W1OitUamktEV2BgKdGqQt/cp0WGVrvE2RNfIWqU+dH9RSxmH+HwcebPOlj46D0UpcfCzQ184zZyeyXuv1MnfiHKuS9O4hw42HziZNDt4bJYufTSy6A2WT5aoVpKoGhBrE0/pU4SQZZYe5TE5glxPhbFOxTjq55xssY69cYKPd8CBnOamdGPYZI3MdjuotadvHRqiv2iht7r4LXoVM1GuoKIc07C2RHpdA1YJCvx6EnC4woHjQxhQwzj4Oefbn4qIG+UWyR/dBxzz0NJSeONGmm1nIxMDPOT2+8hGbv45+1YdT97jfvY7MdoKDkStwYcP2VmZjqGzfoCR4/+GpfJRix0jlKyQi2/gsViwWwKU+jmmJyep+/qIcoSA+s+zYyF1VoFJevD5IH80SEjQyFe+DtzGAwlurklvvLM16g10tzbf4uhISOtwxjXIp/iZ2+9ySBSJ2ieQVGrdMwpYi85uP+NJNuFDHOfeIluqcTf/9VfY+nGIguLbxGKFFDFEfZu52kkIWIboy1K5FodlGIctyPHQFokFPJRVxV+9N1v4AiEsA85cJrsvP4rn8TsgmQxSddowKzu8qScxW1WKLbzGFWVYYOC1+tCkap4a2MMyiIDW5SufRe/1UGjLWFvdlncfUC6VGNoJkAwGqJd3cSgG/HYpvAMB8gfpfnwjT3Urkol3cfjzzJ/rsxoYBKsHV4++xrXzzzHd//8DxDVIr7hOJcvfJbgWIi9vWcxGK28e+tbrKffp22dJJjMMXHyNC0tRTrRI2x4Dqn9CMNggGa4wMr9W5gNZl6afZkhQUZJ7+KfbnCqN8nKUpbA0AlS+2/hMp0k39rk7PFxNkr7+P3PkN18yAD95/L1VMiVP/p3/+TrL//aCI9vFYjHJzBZk4yf8OE3nMHqCzIYOmLIexqXaZh8dgfZYSe7V6Da6RKb/BSb95exuGpExiucfGWc2WMvYXIf0rO1yRwMcNhGsVRb6EUrqeYWnrEuu6tVOr06el7CEfYwaPeZGhoFUaLdajI++yXSXQcPPvoJrXKZ/GaSMVcboTpPvprmYKdMrafy+NYudlOAQq6BTTHxmV/6L3HH4tBTODntomn+MbE5AxcvfoVs8zFG0zqFNTfz5yaJnwnS1cwYjE5Ek5Facp2I7xrmsQLpzoe4J1TSuyWCvjiSsIwrtIHZvENF6dMoCDT7q7itJ2lXMmwk9ig1C1SbHawyGB1B9LIFiyOLNXCKSi9PpZxFPTAgmILsHLUoJhNYQwLNZhaLdQyHYkZpKwxNjdNHx+S3Ui/UMdlVxq7ECA+NMjIk4URHLVcxG2wk87vIwXEWd9/gvR9/wMKHy5TrS+Rb7zAWHyUQOoNifkyjrXJv5wk2axSn0U+/3sMo++nVnXQyDhjEMPVTjEbniI/HUSol8rkuAmEuP3sNoX6EQVRRVSf58i7D7hH29pcYNcjs7a1SyXf47X/4FGvyP/w3//Tr4Ss1Xn7+NSZnL1MwZ2nr6xwL+NnJZmkJywiVMKHYJNisbO/eQXK0OD//WTRbC/dolrDzM0gWhXJvgd3ae5SrR/QqccLu4xh0ODdzio3Ge4hWFbNnQKskEj0+wciJIKNjEdy2CNlBnQuTp8m02zxe/xlqqURXNdCx9ZiNTCJbVARnFEezRTOhkFut4tKcBP1DSNE2Bl+aZ6fnePm5T5Jpvs9h5T6dQZW97jq1zg1SpTySuYtBdWIe8tCubWALLhF0WSk9qaIWFaSmjcYamGwe3K4R2oMdJMc2LklAE46j9xVckoGWtI1Z7mEeVEgnO6T3etAUiEbHsDpcmHUdrV2j2euytnuItS+RWO0xPTrC7mqHsfkZGpYU8alxzGYDQslKJOTHZHXRbLfwBTx0tRpjs2cZvp6iVN+jf2hgLX+bjcQCH97awKSLXL70cQrZFoWWSmK3yGpinYrSxjR6wFbjCUK5SJUMiY0aJ4bOcunCS6wcJFHUBLIEXYMRVVUopnaRHQP0ksyf/tmfsrm/zXw8itI8wNirs7J+iGIoIhsNqGYP5e0Ula4RRaswEF2kdzP87m89xd2Vf/Vvfv/rJ19w0m4PWP1gDa93FqfZRK1aweCuUafGQOjS64m0GlkapRq27mmmpq/w+NYHJB/uYBxZpdQoUS23kIUQtbyG1B9jeGKIgd7me3/1BplUkdhJK0rWgGyV6It5vOjIdT99vcreyh7t6g7nTl7AFQxid04SGAkxNj6K0xtBMRqx59s0Gl0+/ukv4A+6eHl+jvj0WSZCpxnyzvHR1rdY3Hif3dIjKgkdpRElHjlFpyfhN19E6+p4Az1y3VXqisygPsGIW0O2C2gmF1pfwyqYMepRrkbniUXiZKs9lN0OzvAz1A8VbGYNk0Vhd6mBzW5CqvexhobpZIzYXALNZot8s4vYFklm67SFAEgTXL40j26Mkk7uU221MDYdjE2cQe7buHzmWerlJh3jgET6kMLmLkH3EJMTY/SbDdL3qlSbOiOei6S3NTy+YRLtEn6hzpmrr3Ji9BQe3xCNbhqha+BgVcduBrUr0FixkVrr0m9r3Lr/Lr6gG0EqsF0s05UGdHcTnJyfp99pkNpKoVvsdLsVLFYVh9mOL3ice0cb9Hpl2n0FQ9fGlNdPgQ6BrojmH+ZweZevPc3dld/7Z//k6+fmv4AkeKB/xEc/W8Bj8yO7VA5KGTq1LoJqwIkVqyzjcYZ47swX+PDW23jDDZLLGqoaJKzZOSw2+eT136Cru7GautQGCY5PvcbGwhNEQSQ4JfPS9X/AUGQM0Rgmc5RkcyOF2xShWa6RS1ZIVGp09BU8I3kkawxZcjDoNXH7JyjlNHaLGexGIyJduiOTNGpVPrrxNhuP7tOzyXTlFrJoJRZ+kfHpq2hdJ8OOYcbdZ5gIPs+puV/EpM/z2rm/w/b2h3TNG4g2G7Wyh3SyhGPajXfMy63FD3EZdc7YXsaMztaTNbwTJppNMzbpE0xHXwXVglFP0dbMTMTj1JtVrL55SoUiZtMQwaExZk+eZDKokVh9gt07jS9eZTTq5eLlL9FptylnjvjZX/6QjLKJ6twgEKoybppi0GlgK1fZWjogn+8j2+OYYsPofSNWscXIrIcn67skj5L47R68Xi8J5QZjM2UGPSNtLYCS9lKtGTGHygTP27Fa3cgNjb2ciskqM+i5MGa6xDwWbIKTlU4D/6SL5178HHbbDP2mgUBIpNWvY7IHSRwWENwh9o7qeBx+xpxNDldzpNM5/tHPOcL/VBjPgWgl1X3C3vYm9aSffLpLpVJj2DpON6HjN4yR77vZ2Fzk6ut2jOaXiES8WIxGeqpG6BkFtVOhnHcje2z85L0fYLT3CYdmSR0sYeg9YO7sPB3nJpV9hWRgg4N8knMzz+A57cUwYWdr7ybusTAXvnyebOmAem6Xc+NfA0mmUstR0zXuv/99XOIoBpeNpcoKQ/4pHt/9M/ye0+y26phlmflwG0EWKRRKFI0/QezAoFXm3t4useA8k95Z5mNTjF19gf/1jX9KZPw0G9kNrKYVzEEXZ4OXMHkFAjaRQVRm46jDYPo2yvEnWANO6u06kvIsZiN43S4yR1a2Oirjwyfo9x/SbTdIPXwXIzbKDoV2Psfe0Q7FvTxWSeaUd4N0fRFd7HC0fJeOeZirsx8j4DqBFLKytfqAdPoeI1NehEMzmcMyTkeIo8QhSrWA3B0j6JdIlUyMdHqsr2zT69foaiVqhSoDU5uGsUW9LhGwxTn74jnurN5DDlhJ7RcZcgQYeP1kNnaZ5ximSTf9425ams5GJYnFVeBgd5WAInE8dg0xHmYpt4ZcbWEUZebCo5RUHexNmpU+2UYHh98H+s83nk8H5HqHg80EqVUHokVAHuj0LX3eWf2IyZkBgdYU27e2UDGwvZfm5HGVRrnA1NgQiubmyF7AZLBgGOiM+52cmPw8P/zpt6hoOuPW13n43l0c7jp+bxNXcIRCLkNPy7KV/iuG3CEuXfzPcfpVRIdGoWHF0ElhFKzojRXW90tsbj5E0EcRbHZ2jnYxDVcZCGEer9/HO1LFFdjnsvMsvuEk5d429CSMkhGT5uQg8xbGThhBidDrNfn+nX/Nh4vvMjPk4ODokNc/ZqJRhyYSeibMC597Fb9rhP3DNfqDAV/49C/x49u/RTIVwNm9TL2aIWJUqLcf8+T9H2A0utDKEdYPFjDau2Du00wLmNxtJoc0DitZyjmZTl0jMDPGYWKVGeMlOoLCva1Nxi5L3Lp7E60scv7zr2CKJBj3X8I1/jJjsyLqwjKiy8xLXxynLxrZWV+lLVqJDUdALeIyOlHaGl2ThdjZaco79zlYbOP3TuORTazdWGT98QHOKTfd7Qr6xB7RcQvGqo42LhDx+Cm2d9jPJ9hKZaimm7Q7HagsIYgNllZ3OHPyAjnRR7M4wNwoUnYkmAqPIxosqEdL6JIZp9X+c/l6KiDvKj16HQ/hYQ/JlU3sPhtCv8f0SSf72zWW17KEp6dA1Cj1+uzlDhiY7ZidTbLJEvntDEqzysTMMAZbiBvv/IRnLn0Mf3AIjCItY4n9nbepVxvY/V1k82naB+8SG3uRiHOK+w+/R1pfJ6ie4tzsdd7PPSJZCvLew9sYXA5k9wl6iky/lcEyLNLpC9TTO1w5+0VUNAYsY7A+xMQcbtXL3n6RsrFALzlA6AcYuPrYVQte2ULf2aetizzYLuAeOeR2YoOaABbZQLVe4dvv/Q900yZ0RcBkjdHa/WM0z4D0fR3TZIljMxcYVMrU9t+mXClhLvbpmI1ELKNkMilqWhXBYMRgMFNqtxk0TPhNBnrhMAR6tA79mGdljg/H2M/mSe3uc2nyEnqvxvvfeANxMo/Ff8RoIcjeVpYDNY3zyIAldptAII7DE6K8VcPuEbAbzFy7/As43Dbu/PRHLNbfY/b6RST5DIdr+zhHXBy19jnxGT+iPKAqHkMvFDg1c4lTM6fZzx5ROFigvFlGavd49uR1PszcITgEnz3lI2Fq022qHNYLmFpGjJIFl2Tk4KjJniFFxO7AG51A6goo2lO+NCFbDYOh6DCFVBmj3Y07aOLC58c4Okiw/sMCVoON8Jk48oiIWKkTmHTRKahIxh75hsAz4xaW7ldpdOv4J4bJbjX4zC99AYvdSKmxQ6G6hWy3cP/2Bp/91Fd5ePc+BnsfixbDHjLSP9pk+PhLBMJRfvDNv6DrKGGzx3HYRQyDIsbuEJevP0/i6AP0fp/F9Swn5oZQBw08apStRgWDN4tvMMcwdgx9jRut75BdrdHrW/GNRvE55hgKWkkUljFYwlQSu5w4NUle+ZByo4CAi6B8grnwLIHANdbWf8S3/vhdjl85icstsfj4ELtdYXR0gkauT3rzMbYxK3Gji2RbQGvWSR5lcbglNEHCOxZh+tIFNKFHSLbT0dZxh84TGznFiKGLu13g9//i+yyu7fLV3/wiM54Jihaotuq8853vk3x4wNjFaebPxNHLXbZ3t7FPFoiHPoZkiJIqrxKwSFhVK5maSq7cp29QUbpHDJpGRkedFA4TGDUPeSFDxO/DbR9B9Gq45BCT0RFWP3yEamkw1AoQCxrohQMEpp8hefePaWTSLLncWHujmMJO7AYLg56do90FNMWE7nfhlOuY6x10zcrDW8tkMoWnd0Dr9/7F733dezxOp1bn73/l79E2NsBnYuN7h1gCVqaujuMdkTl6vE833ydywsJzl75AupoBYQ/X8Bh26wmK7SLm4QiNfJdGeYtiP89B4ohqtkfEKjBQGth8h2jCgF8Mj5LpCaTqhzRULxa5zm7ybe69tYTd4sBktTBscxENz+IdtvDXP/sz9ipPqBY1tLIV74SL8MR1yq0moWAM2RRgOjLLvXd+yGb1iIElh6L1iEamaXRU/PZxVh9/RKmdw9RRMMoHbCwesfewjaRZuDz7CnaGKLVT7OzeRVNSpNJdVEUkdv40Nr9AxyxxbPoquP24Y0Gkgki5kcTeGLB5mMfYlxiYBXpmibA7RFtpEfIFcIU8dBWRfqtH2OVhVFLQjR0G9S67rT7GQY/g6BhuTxiH2YQumDh+5hqyQccrBdGMTloDA27pGM2WxPKtD/EORKaPX8Bjc5HMZTj3wiUsFjMbb99n9vI8Ts1GXc1h6g2Q2mZK+TrFVJHCSpJqDSqlFjMX50jnS7R6Biyixq37DzmqNnhpLsKHSxV8ky/h6FQpturUEzU6lTZ2ixdFU2k3EqiVFsfCEYRBl/WdLL/zW3970sRTAfn/9Af/4uu+0SBWp5tXPv0x9nu36DZlauUUar1PTxtQ72aob8CXP/0iu61thI4ByW3Dba0T1OfxB8ewjUYY9s4hmJr0hC3q9TSFhx2Uno6syUjhPE8eVjhtHeLqaJjv3limaa2Q2dnGZkyh9MsMlADnnrnOoA8Oi0qHJss375A7UugJDtSWneLRAe2ymU88+zL+oSgDVUdX6tz56bvMXhonldumKZZBmqCSzSBZFETNQtBvI52o0G01Ufsyes/EwBChlRCotmvcun0PQ8TJ6tI+JmsMKQwHi7tYnHkUtUe91iQ6NEQ8NI7RbkSQBnTaRlKVCmqrjdDR0QciDpOMZ7iL19HCNJqmPYDlnQwHdxewd+qcuniK/kDB1NJZWFpBNLjollOMHR+j3Qb/8BjOsIvgWBilWCedOiQyPY/ZFUQ322g3W0xPzxKMRGnrXVAz3HrnNiGTwNT5KEq3T90cYtCXkcb6OJs2Ug0Fu9WBWQRFHGD3RdhYv4lveAIGIuNhJ6pcZ2lpm0GsxO5SFbWhcPXMBUwmEO0yT5aT0Gxg0nu0RYFTkzE2jkrUXW7S64d87bef4kVmo2TAPRrhfHSO9ewq7ZKFjtqgV9OZPH0J12QDg34FKdZE960gNUVMHYFCY4lMJUPRdBeT3YVUshF7ZoSid4xmQ4R+ikrxgMmzZgxeC7nDMIHIKC+9PsVf3iliHwe91iHgc9O3Dcjs9vBN+6lp2/TVEM1SCs0j0g+3OHPFh6MfJ7sh0vGYcDglDvZvsLy6Qrtuwj9rxzEe46ON9zhz5gq3F+uM+ofQJ8cppPdY3l3G7uhgdQ3jModRRZXC4TrN6i79gQO5MMCouZFUGVEYJpOtc3zOjP5xiU62Qllr0e87WHzvx1x4+VMk9x5hsg2hW92otUP8s3MYTAYamxm8U1FylW3qOQVHRSV4oUV0ZIJKacALz57HJZopGbwY5RJf+bu/hthc5dvfvkGpmEUwSIgGB6PxCKFQnPET81hDUVqlMqGYm2ynhf+5GEbdwPrmHYo7SVz+MqdnhhHMHu6+9QHOkQijsSCdnpFczUpo2oSc3iaxXcA1biLidBEaDlAoqqTyOajAthDklH8W14USFlRcQ2H+ky98nrWFh6yWMoyHI4xZ3BjtJl59fo6OyUkrucWd7i4XJ15hmds/n6//H1n+fygBa7vO89cv8Yf/87+i7mqgZQeYrF78cR2LYwaroU+5Xmc9o1GqitwqfYQi9QkEJtElG+vvbjH/wkU2HrxF02Dl9RdeY7ecIhrbZCw6j657KFTe4PioiQ836hwqRvJKj+yBQGxKZGOpw4T/PK+f/RQfbHyfF19/ltX1Ftu7mxiDLWrdCmqvyeUXXmcn22Nm6DwRZ4C/fvcGz52bpCUDLhO9HQvFQhm970Ip2kiWthDsOaLTTqwGmaNEgla2Q1UvoyTbmOICTrOHnrmIxylRb9ZplSv0FJm7ySqTl4dI1Ku4/G7aTRP2MYVCe5X921lET5O+YGXm1asExsO0Wi1qEzPIWgN9JUnb3kFI+zDeb5MuP0DSQdF7NDpN7BY3FUeXtY++SanaR+koHGxVsHvtlNMp1rf2EJvvIktmnC4/9oCDtYU0mOx4HUY2V7eIHDuH3+Yjn4TuIM1k3Ee/JlBX8hwqJSJTo+iChGbM4rkC3sM4pjKYZT8dpYVvdAJdFGhU26QVlfG6gNkmIbcj/OK1UZZvfp813YhlZAKHz8a1T0zTOkywk93FoamUdDdXLr/I9OQcP7XJP5eupwJyi8WK1+xm6fEallqX4bkZyloXaaaE2e6nV20gODV0qYVY96CUdnBEogwqRQymNk6/wtjxMbLJNOlEmkZOQ0+V+diXvsChpJJOPSFxUMIvg+QI8L1v3cAW+j/aObMYua77Tn+n7q1935fed7JJkc1FpCnRshPHsSIHg2SiCZSX5CFPMy+ZeUkcDDBI5iVIAuQhmCDOADEwCGYCL4lhw45GiylqocVVza3Z7LV6q+ra96q7VN2688ByQAQSEBnRdIvoDyjUuf9zUedr4Id7z7lL9xBRFYuws3S1jByMs9otE1i+ztZem8aVP6fnLuMfGqJZnKZXb9Ky1HjUuofFX2Nl0yDb9eAKp/jw2n0uvvIlGtU2CXeIliZhkV3klCzT5xOUqjpSv0m9DKrmRPI78QsPpW1BrySjB0v0Km1Um5WRYxN0xryUlvdobDVZ+JWLyME6J+OCh7YMmrLLyrsNTLcdh9sklBjBbpMoLS2iW4p4XeOYhgU5MEHUbqVZy5Lb6mJVAvRsEPR5sFqdIMkUa1ksyRG+ePGrzI4tcv3eTVptHY/LjxcrXbuV0USU9xeXkfNe6HVxRTWaQfBIUQr3tigFTTySRD/g4sM3PiSU8jM2maRSruJuyiym7+Afv0DAF6I35yFl9TM6/0WUdh7rnhXTUqVVzhCZHCVfV2jYJOYdId68+gH2qA2PpY13v0ujvYPoNwh6HGzWitwoLxGTz1Ha3ELN72P2lE/M16EIea/V4vjsLEorz8nLJ0lrGTzJBhE5hrduxeW3k6uuMeaKYJmewp5KoLXaGA4bCJnNayUSx10kQ6d54XKArb0y6Xt3+NFP3sYXMnl8f43RoRSnvnieb/33vyOfrpK8HCHglmkUoK85mRyJkExOk4xP43HGKPEmJVXFGTxGr9SlLtr0rRY2ymlifYPcbg7fMJhmjGw7z9W710l6LxEbW2Anu0TQHgK/Sm3/On0jxPnhl8nZShhmE91eRxZxxl4dI7u2TlXbxqMP4w6r9MtNjk88h/e5y1QKJYY9UaqJO9x8uIKcNCndD1PZbyB0FWRojrSo7BWRInbCCRdF/TZGx6C+C15PBE3vMH8syWu/+Tzb+TZYXCi6jsPqoK1KuD0nEP0ODmcVLajgPeZBbnYw3S3c5dN8ZeF52mMVNGeT0dB51hYLbK1lCCejRKcCIBtYrTp+b4jZ+bMsPfyAK/fu4g96cXoEMdsQsclhbCZ47GXcVivG/jW2tjcIBE+wlynjbuqo6TLBs88REG06jzN0DI2E1Y8/HGRu6iI31q8RGRuhldvCsPYJuf30s3nMvkZ0dAij98lXCQ9FyG2STMAbYm31Cl3JT0/Rnvwrh1YOo7WGWa8jdCfToWM4eg08rT6y5MHnn2AkOYTrC3GGIxGKSpluq0k2s8cv/jMnF0gAABB8SURBVMp53OEonXqde29tcPY3zrN+t4jkcBJO2fENg1+O0o1bOflLUxiWJw/0L+a/j6poeONlpiZeIGgbITBU4z2jjVkzcAT9lNfW0bwajV0nmr5FIOGiWy4zMxHg/Z++TWJmHJfbxsMrK9S3PQz/YhSBgWQ1OXVyirX0Y3Stxe7q+7TTDs688sscmzvD1tKPuH3lIdMXJNQytFo6YYeFysM0rYZENAlytEY/3UNTDOS+RM3VRh5OYVEUul0Du6tNYcmkL8k4rBI+p5PQcAwh93luOoVBH7MrMHpdKq0ub3/nH5mbc1PKtrCFUljdfvxOH3vZ9/GMtQifHMdv2Cm0dij3rxMJn2FlcYP08gZyf5SI3aAfaTIy6sPoVvCOuJkLqAi3nVh0CGuwjF7NEIiOE41cxilX2Ml8QKm/zFbuEVabh0n/MD11jf1CipMzQdbDHSadE6BqaF0Ly70yG+1l1I0szn4dmy+Et2kn52rzqy98jbXHdzCN3ifm61BcXfmzv/jzP4pMBfD4Rwk7XRQaFYyug0DQx/jkCKX6Pm4txsunzlPMPGI3vcoui1Rrd8nnbrO7tcrcUJBqcZdc7RGStU2r12V0YhizbvKrv/4qk4lZYrEYNofBRuYBM2fmsLlGmZo+y/zcMZQOCEuLVtPE7rdQNxpIWh+bXbBdTtPJ7aNUZPROmXLZhqr7afWtTCdmOXfmK/RbFuwWgaJ3MKwqdt3kwi9fpCnKJEZm2N/tkApJfP+7b+H2hfCFbUxOv8jC7ALTEwmq+1l2cotEx5PIfpX1nQzlyjL1wg6x6ROExhNUmmlCoQnqzTxmC5JzUwxdPIetB2pfpadlUJ0O9JoXTBlPzIdvIoLf7UAYVVzeEHabhORyYZFl7m2soYo9yuYuNVsfydDJ1bOYXZULv/AfuH3tNj+5/SaKWkGpCqLuGfIbO7S7LlC6YPaJ2n34QnNsbN5BNcFsOrkwf4p+O40dGHVNkAgmSO88ZvneHda2r1BqFigrXWKhMcK+GIqzjSTqdK0OFEcA4XLSl2rEHDGKe7u0+n2sssFutYC15sdaU3B3nRyPHufySy9y/9YjNraz/MHvf/xTiIfiSI5kYTo1zsrSMjlbi2KjTjL2HF7RwdAtnJj9JUqrd/n21b9iJuImFZEZP/3b7K7fo9y6w/h4n/XaGpmCxuZak2TIxvTCBPmVPXTdQr90n7Xbefq6zF7xMce/9mWCkhVvIkrU52Y3s0S/r+ILBJmMeXh/+XUks4NFGiK9vIswvVglC317na5s4YWzJ+nio9GocPncCbrChno8xn7xbSSHjX7HjRqykd2/htIrohlh7KNWPDaJ6NiTl537DQuWwj5aeA/DjJMpb9Mzq2iqhUa2QSIZIC/p1Hp5Zp3QMBz4XG5M3Y4/YefixTN86Rd+gy4yj7ffp94xWF/WcbnnsCacNPezBMN+mruPuFFQyTeHSB2bwqw1cbiDbK6sUWuBOxqk0a6gVWXCpyM4qyZbH+7ikd4k5LTQJYK1Y8fetaOtWjk2PIcn2GD5IwWj18Hw2rG4fBTv2tG0fdpaAKPXYGR0jEahTmnzJtGZIdxGEa1uJV0SzF86x/zYFGNDJwjJPVbTaQq5LMW1XYRaYiTlwCpSZDwNinKV+kqG1EgSRz3CT28sceZ8kFAyTnhuivduXSVXr9Drf+x9IOCwhNw0EKVVCvubqDE3jkYLz7hCR6/TXO3it7rZzvXpe3RWVTvYKrTe+jEBgviTc1TXLFiDKqW1dVx2UCQnP/z773FxfoFOTOL+99/EbusTSY3iCVsx6jmqjhlayyvMLni42/yAhlIn7lkgX9nD73Ki9pNIuGjWslw+e4FKr4busKHoi0jmNqZ0EgPBP13/W0bGrXREi7aqMZr4MmulHKXsTVqBMHZ/n1KmQThuQbNPEI27cPlVHD6BqkusfLTDXl1DVpqkQpPc2M7gtjrROxWKjzy4/TrzL8bxJ2f43gcPyVWLSN0g4/FTfPjDN1jOfYTdWcWImVjMBK1yAVXZovK4hhk/ganaGAmFWXp3hwujdzn9/AWanSo/vnqFotRlIjjD8FCcD5o7VJpBhoJRLME2fZeGvWbD2FMwk5AMe9ha2mNnVyPgcnDp+SlW0jsEpsYZmjhOq9Pg/vu38C0USGd0Ht5MY/QFgYiN4bKBP9wldeYEcfdZEqEEdbVLJrvCGx+8js07jk9RCVokJEkms7qP32an5XBg8UUxLLt0DB8Wr4I1LOgIcHcMVrP30CQV3dnDPOyvvxn0yfR2iaZG2NaajJ04Taa9TTOXw6IEccRGmZocYbPRpFov0TJU7IaKMMs4twMUbRK6IdHzt2lSoLLlRt+1o8ybqDU3ZmUGq9Sh61Qoqx1oQLW7hC73uf3wfxHw9jjx/AzNXo+amqRVzxPxZanVGlw8N4s7GSLatVM2ZPbKfVq6Sb/Tx2H40cpnuZe9TuKESW3TgSNVZWsxhy1sYOm58IYkupKBRQ6xXy5Qyhfw+ev0qx3M+hbCP8HO43vMzU7So894dATbRAhfbx/zbBtRllhMrzKmCXqKTDgcQLe18Y60OT9+mgd/cpOybEK+C9UKXreJqftwWl2YNise08F/+b1/x3f+4W0CoWF6wsqt965y5/oKjik3kiHxQmqW0WHIFJpU9z/CPhOnbnYxgxa0jEl/t0pqfoHZsTlENc/V169T8zaZPPUiSU+SgN5jbHQG+etB6vIq9bZGtb2Gz+ViOHEKWbSoZRSGwxL55iI3azKj4QViHg1/SGL3UY74eJSp0Ti6A6oiRkOUcVtkKqodd9uLHNApNFqMT4+g9WvslDp4vRJqRaHZVTA45AtPWUgU8yYpd5fExBmWa4/prOaxeoKYdh+Z9H084RCSYkFXE1CRKKgZLNU6M2MBgnEXO8093AmgNU27U+XXXv0iRZ8Ppxzjq6/O8N1v/TWJ8QTBVA7v+EUmbVHufv91RqYCKP4i++Yidg38/lmaOR+Kp0G5ncOr+YnZZXSpS3b9KtuFXSS7nXLhIe5qGL2rUcmH6Taa9FpQr+1QzXbpb/dxn4kixUbRzDI2R4qHm9fReyaNnTb2GJRzHfr1bYKGg9hcHK8zgruRRutWiDrtyFMu6jE/+W6GMf8wJ0/qrC6vM37yOI/Sd1h/q4CZ1PE6DCrbNtx2O8cvncDmDyF1nehuN2L1AVa3h9de+xoWR4Cd7CJ33n3IfCqOzS/T3S3z1tqbJF48xeSIj129iFdKYchtDEcVJWCntqZhNNuEpqYYOncKHAZq2KTLChWbDXWnCP4QrpBOe7vNVMpNvH0MWXTwpPoU83V0VWf17jplpY7ohehcUqjXBa9MXaaerFLsWlnVPfQ3N3jp3EnyLZNGv8+c7Ga9aOJ3uuhHVKyqE7se5HGrgKS00bUOyraC6B3yR20VtYHDFeanjSxBh0l5ZQ+rzUd8bJJibYUJb4yW0iG9XiESTjB9coqt7S5WtwXVNJFrHaqtOk5PhEq6hmbvkW0sYrNMMTaa4MHiNcZnJgm4wlQqOj37LV6a/hKxVxPo/iL7HQ1NQEvr0Ss/plQMUdkSIMvknE1uGn9HYb9Ge0/QdbuxaGAFzl48weqDbbo02XpQJHUqSSNfJz58nJ6lwcmFcfyxSZbTOq16iepOhZDLy+bdNmZLoW+4iA/7kAJd1HqJl59/jZ7Z4EfvfJPVPRuxqTBjqQC64eXBgxtI6IzFIpwYvkTeE8Dz3DvcV9uYZT+TvhjD8SBLH9xCswr0so7VZue5C+f5ya0H2JMRZoMaP31vib2ajF+0MHMWvMJCtWOj8u4yJ8+fZnh6HkVpYzX95I0GU3MB9rs9NIeTN//PD7BLXayeKorUwmibKJktRidixMN+6t4q2oaFu+0dEuEAcZeH9MoKjbbOeDKK0rJgWLrEJ4Yo5XaYHJ2joVlQAzE6uRZ6dpN7qxvMT8wR9vqwdVTy2T10vYXF5cOyWaWilAgGwgSsCUYnRvno1v8llgqzv1v5xHwdipBjCBBuLFKYgGbgnxijSJ3GShqlo1JQmmw09kjEHQxddDI1dwr/uo+9pesk/UF6XhudqqCab9LQ7DgdKourWRYWUiy++Q7pUobZ488RD9gxx8bJK0tUnSuE3AFW0wbJ4EtcufYd7NYUarvDQmQSwk4er65jqIKAnsISWqYdrJPectLr9FEaeVYe3ic+NI3aqRCJxbnw5Zd579s/4Ou/9XVMi5VCJsPm42tkN4tYIj2Ey0OuZsETnEQVdZwBF76gB1dEZWetzPdW/gcnv3IOl8+PU3axn95HsuwR8njZXd2gZzWpK8M0yz8mXyvjoIC/4qKJk4VzQ2xu7iD8fRKzQepZA4cWZ2rhDA43VLY3uau62K6qjHw1gVZTMDQTq+Tj1OkUxVydalnFYe1ic/vo6tAt9gjNWXh+/t8zMnqM9XOXefe9H7F1S8FQNERPRikqZHslRsb9ODt9TpyfYfMjme1CA8OqceLUJYp1BYfLpG8p4LeHCAW8WG2j+GZmqO2VePDhOuH5MabOjxIdH2Kv10FJ16k4bdzZ3iY27KKryziTQxQWt4jMPo+tUkTdyiILL5PHhnl8d/cT43UoHrUVQjSBlYP2+BREgNJBS/wr+Ty5ws/vO2aaZvTjOg7HkRxWTNM8f9AS/1qEELc/L76fJ1f4bHwt/5Y/dsQRh5GjkB/xzHNYQv4/D1rgU/J58v08ucJn4HsoFp5HHPFZcliO5Ecc8Zlx4CEXQrwshFgRQqwLIb5xCHy+JYQoCCEePlULCSHeEkKsDb6Dg7oQQvzlwP2+EOLsAfiOCCHeEUI8EkIsCSF+77A6CyEcQoibQoh7A9c/HtQnhBA3Bk7fFkLYBnX7YHt90D/+cw1smuaBfQAJ2AAmARtwD5g/YKeXgLPAw6dqfwZ8Y9D+BvCng/YrwOuAAL4A3DgA3yRwdtD2AqvA/GF0HozpGbStwI2Bw3eA1wb1bwL/cdD+T8A3B+3XgG//XOMecKAuAW88tf2HwB8epNPAY/xfhHwFSD4VqpVB+2+A3/q4/Q7Q/QfAVw+7M+ACPgIu8uTmj/wvMwG8AVwatOXBfuLTjnXQ05Uh4On7sXuD2mEjbprm/qCdA+KD9qHyH5zOz/DkCHkonYUQkhDiLlAA3uLJmbxmmubPXu152uefXQf9dSD8acc86JB/7jCfHFYO3SUpIYQH+AfgP5um2Xi67zA5m6ZpmKa5AAwDF4Bjn/WYBx3yDDDy1PbwoHbYyAshkgCD78Kgfij8hRBWngT8f5um+Y+D8qF2Nk2zBrzDk+lJQAjxs0dMnvb5Z9dBvx8of9qxDjrkt4CZweraxpPFxQ8P2Onj+CHwO4P27/Bk3vuz+m8Prlh8Aag/NUX4/4IQQgB/CyybpvkXT3UdOmchRFQIERi0nTxZOyzzJOyvfoLrz/6GV4Erg7PSp+MgF0kD31d4ckVgA/ivh8Dn74F9oMuT+eHv8mQe+BNgDXgbCA32FcBfDdwfAOcPwPcyT6Yi94G7g88rh9EZOAUsDlwfAv9tUJ8EbgLrwHcB+6DuGGyvD/onf55xj+54HvHMc9DTlSOO+Mw5CvkRzzxHIT/imeco5Ec88xyF/IhnnqOQH/HMcxTyI555jkJ+xDPP/wO8VJRjp2VixgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Greedy: black and white dog is running on grass\n"
     ]
    }
   ],
   "source": [
    "pic = '1523984678_edd68464da.jpg'\n",
    "image = encoding_test[pic].reshape((1,2048))\n",
    "x=plt.imread('Flicker8k_Dataset/'+pic)\n",
    "plt.imshow(x)\n",
    "plt.show()\n",
    "print(\"Greedy:\",greedySearch(image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "q4ogNQPCPLr2"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Automatic Image Captioning.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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